Publications Samples in AI, MIA, and PH

1. Imran M, Krebs JR, Sivaraman VB, Zhang T, Kumar A, Ueland WR, Fassler MJ, Huang J, Sun X, Wang L, Shi P. Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge. arXiv preprint arXiv:2502.05330. 2025 Feb 7. link 

2. Durante Z, Huang Q, Wake N, Gong R, Park JS, Sarkar B, Taori R, Noda Y, Terzopoulos D, Choi Y, Ikeuchi K. Agent ai: Surveying the horizons of multimodal interaction. arXiv preprint arXiv:2401.03568. 2024 Jan 7. link 

3. Du C, Wang Y, Song S, Huang G. Probabilistic contrastive learning for long-tailed visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2024 Feb 23. link 

4. Zhao D, Wang S, Zang Q, Quan D, Ye X, Jiao L. Towards better stability and adaptability: Improve online self-training for model adaptation in semantic segmentation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2023 (pp. 11733-11743). link 

5. Team G, Anil R, Borgeaud S, Alayrac JB, Yu J, Soricut R, Schalkwyk J, Dai AM, Hauth A, Millican K, Silver D. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805. 2023 Dec 19. link 

6. Xu X, Shen Y, Chi Y, Ma C. The power of preconditioning in overparameterized low-rank matrix sensing. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 38611-38654). PMLR. link 

7. Wang H, Ma S, Huang S, Dong L, Wang W, Peng Z, Wu Y, Bajaj P, Singhal S, Benhaim A, Patra B. Magneto: A foundation transformer. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 36077-36092). PMLR. link 

8. Wang W, Bao H, Dong L, Bjorck J, Peng Z, Liu Q, Aggarwal K, Mohammed OK, Singhal S, Som S, Wei F. Image as a foreign language: Beit pretraining for vision and vision-language tasks. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 19175-19186). link 

9. Yao Y, Gong M, Du Y, Yu J, Han B, Zhang K, Liu T. Which is better for learning with noisy labels: The semi-supervised method or modeling label noise?. InInternational conference on machine learning 2023 Jul 3 (pp. 39660-39673). PMLR. link 

10. Wang J, Ge Y, Yan R, Ge Y, Lin KQ, Tsutsui S, Lin X, Cai G, Wu J, Shan Y, Qie X. All in one: Exploring unified video-language pre-training. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 6598-6608). link 

11. Su W, Zhu X, Tao C, Lu L, Li B, Huang G, Qiao Y, Wang X, Zhou J, Dai J. Towards all-in-one pre-training via maximizing multi-modal mutual information. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 15888-15899). link 

12. Selva J, Johansen AS, Escalera S, Nasrollahi K, Moeslund TB, Clapés A. Video transformers: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2023 Feb 9;45(11):12922-43. link 

13. Novack Z, McAuley J, Lipton ZC, Garg S. Chils: Zero-shot image classification with hierarchical label sets. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 26342-26362). PMLR. link 

14. Radenovic F, Dubey A, Kadian A, Mihaylov T, Vandenhende S, Patel Y, Wen Y, Ramanathan V, Mahajan D. Filtering, distillation, and hard negatives for vision-language pre-training. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2023 (pp. 6967-6977). link 

15. Müller P, Meissen F, Brandt J, Kaissis G, Rueckert D. Anatomy-driven pathology detection on chest x-rays. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2023 Oct 1 (pp. 57-66). Cham: Springer Nature Switzerland. link 

16. Mirza MJ, Soneira PJ, Lin W, Kozinski M, Possegger H, Bischof H. Actmad: Activation matching to align distributions for test-time-training. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 24152-24161). link 

17. Luo X, Wu H, Zhang J, Gao L, Xu J, Song J. A closer look at few-shot classification again. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 23103-23123). PMLR. link 

18. Long Y, Wen Y, Han J, Xu H, Ren P, Zhang W, Zhao S, Liang X. Capdet: Unifying dense captioning and open-world detection pretraining. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2023 (pp. 15233-15243). link 

19. Li L, Spratling M. Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing. arXiv preprint arXiv:2303.14077. 2023 Mar 24. link 

20. Koh JY, Salakhutdinov R, Fried D. Grounding language models to images for multimodal inputs and outputs. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 17283-17300). PMLR. link 

21. Kaul P, Xie W, Zisserman A. Multi-modal classifiers for open-vocabulary object detection. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 15946-15969). PMLR. link 

22. Karani N, Dey N, Golland P. Boundary-weighted logit consistency improves calibration of segmentation networks. InInternational conference on medical image computing and computer-assisted intervention 2023 Oct 1 (pp. 367-377). Cham: Springer Nature Switzerland. link 

23. Hu Q, Chen Y, Xiao J, Sun S, Chen J, Yuille AL, Zhou Z. Label-free liver tumor segmentation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 7422-7432). link 

24. Xu X, Shen Y, Chi Y, Ma C. The power of preconditioning in overparameterized low-rank matrix sensing. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 38611-38654). PMLR. link 

25. Pethick T, Latafat P, Patrinos P, Fercoq O, Cevher V. Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems. arXiv preprint arXiv:2302.09831. 2023 Feb 20. link 

26. Liu Y, Zhang Y, Wang Y, Zhang Y, Tian J, Shi Z, Fan J, He Z. SAP-DETR: bridging the gap between salient points and queries-based transformer detector for fast model convergency. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 15539-15547). link 

27. Hang T, Gu S, Li C, Bao J, Chen D, Hu H, Geng X, Guo B. Efficient diffusion training via min-snr weighting strategy. InProceedings of the IEEE/CVF international conference on computer vision 2023 (pp. 7441-7451). link 

28. Chen M, Huang K, Zhao T, Wang M. Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 4672-4712). PMLR. link 

29. Axiotis K, Sviridenko M. Gradient descent converges linearly for logistic regression on separable data. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 1302-1319). PMLR. link 

30. Taheri H, Thrampoulidis C. Fast convergence in learning two-layer neural networks with separable data. InProceedings of the AAAI Conference on Artificial Intelligence 2023 Jun 26 (Vol. 37, No. 8, pp. 9944-9952). link 

31. Liu A, Bragin MA, Chen X, Guan X. Accelerating level-value adjustment for the polyak stepsize. arXiv preprint arXiv:2311.18255. 2023 Nov 30. link 

32. Lin X, Yan Z, Deng X, Zheng C, Yu L. ConvFormer: Plug-and-play CNN-style transformers for improving medical image segmentation. InInternational conference on medical image computing and computer-assisted intervention 2023 Oct 1 (pp. 642-651). Cham: Springer Nature Switzerland. link 

33. Li K, Wang Y, Li Y, Wang Y, He Y, Wang L, Qiao Y. Unmasked teacher: Towards training-efficient video foundation models. InProceedings of the IEEE/CVF International Conference on Computer Vision 2023 (pp. 19948-19960). link 

34. Knyazev B, Hwang D, Lacoste-Julien S. Can we scale transformers to predict parameters of diverse imagenet models?. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 17243-17259). PMLR. link 

35. Li H, Lin Z. Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O (epsilon^(-7/4)) Complexity. Journal of Machine Learning Research. 2023;24(157):1-37. link 

36. Wang Z, Balasubramanian K, Ma S, Razaviyayn M. Zeroth-order algorithms for nonconvex–strongly-concave minimax problems with improved complexities. Journal of Global Optimization. 2023 Nov;87(2):709-40. link 

37. Arjevani Y, Carmon Y, Duchi JC, Foster DJ, Srebro N, Woodworth B. Lower bounds for non-convex stochastic optimization. Mathematical Programming. 2023 May;199(1):165-214. link 

38. He Y, Yang G, Ge R, Chen Y, Coatrieux JL, Wang B, Li S. Geometric visual similarity learning in 3d medical image self-supervised pre-training. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 9538-9547). link 

39. Han Y, Zhang L, Chen Q, Chen Z, Li Z, Yang J, Cao Z. Fashionsap: Symbols and attributes prompt for fine-grained fashion vision-language pre-training. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 15028-15038). link 

40. Gupta T, Kembhavi A. Visual programming: Compositional visual reasoning without training. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 14953-14962). link 

41. Evron I, Moroshko E, Buzaglo G, Khriesh M, Marjieh B, Srebro N, Soudry D. Continual learning in linear classification on separable data. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 9440-9484). PMLR. link 

42. Dima AF, Zimmer VA, Menten MJ, Li HB, Graf M, Lemke T, Raffler P, Graf R, Kirschke JS, Braren R, Rueckert D. 3D arterial segmentation via single 2D projections and depth supervision in contrast-enhanced CT images. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2023 Oct 1 (pp. 141-151). Cham: Springer Nature Switzerland. link 

43. Dehghani M, Djolonga J, Mustafa B, Padlewski P, Heek J, Gilmer J, Steiner AP, Caron M, Geirhos R, Alabdulmohsin I, Jenatton R. Scaling vision transformers to 22 billion parameters. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 7480-7512). PMLR. link 

44. Chien E, Zhang J, Hsieh CJ, Jiang JY, Chang WC, Milenkovic O, Yu HF. Pina: leveraging side information in extreme multi-label classification via predicted instance neighborhood aggregation. arXiv preprint arXiv:2305.12349. 2023 May 21. link 

45. Chen S, Hou W, Hong Z, Ding X, Song Y, You X, Liu T, Zhang K. Evolving semantic prototype improves generative zero-shot learning. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 4611-4622). PMLR. link 

46. Chen L, Xu J, Luo L. Faster gradient-free algorithms for nonsmooth nonconvex stochastic optimization. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 5219-5233). PMLR. link 

47. Chen F, Fei J, Chen Y, Huang C. Decoupled consistency for semi-supervised medical image segmentation. InInternational conference on medical image computing and computer-assisted intervention 2023 Oct 1 (pp. 551-561). Cham: Springer Nature Switzerland. link 

48. Chen F, Zhang H, Hu K, Huang YK, Zhu C, Savvides M. Enhanced training of query-based object detection via selective query recollection. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2023 (pp. 23756-23765). link 

49. Chen C, Zhong A, Wu D, Luo J, Li Q. Contrastive masked image-text modeling for medical visual representation learning. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2023 Oct 1 (pp. 493-503). Cham: Springer Nature Switzerland. link 

50. Chen B, Ye Z, Liu Y, Zhang Z, Pan J, Zeng B, Lu G. Combating medical label noise via robust semi-supervised contrastive learning. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2023 Oct 1 (pp. 562-572). Cham: Springer Nature Switzerland. link 

51. Team AA, Bauer J, Baumli K, Baveja S, Behbahani F, Bhoopchand A, Bradley-Schmieg N, Chang M, Clay N, Collister A, Dasagi V. Human-timescale adaptation in an open-ended task space. arXiv preprint arXiv:2301.07608. 2023 Jan 18. link 

52. Basu S, Rawat AS, Zaheer M. A statistical perspective on retrieval-based models. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 1852-1886). PMLR. link 

53. Allingham JU, Ren J, Dusenberry MW, Gu X, Cui Y, Tran D, Liu JZ, Lakshminarayanan B. A simple zero-shot prompt weighting technique to improve prompt ensembling in text-image models. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 547-568). PMLR. link 

54. Ghamizi S, Zhang J, Cordy M, Papadakis M, Sugiyama M, Le Traon Y. Gat: guided adversarial training with pareto-optimal auxiliary tasks. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 11255-11282). PMLR. link 

55. Kanai S, Yamaguchi SY, Yamada M, Takahashi H, Ohno K, Ida Y. One-vs-the-rest loss to focus on important samples in adversarial training. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 15669-15695). PMLR. link 

56. Feng Y, Wang Z, Xu X, Wang Y, Fu H, Li S, Zhen L, Lei X, Cui Y, Ting JS, Ting Y. Contrastive domain adaptation with consistency match for automated pneumonia diagnosis. Medical Image Analysis. 2023 Jan 1;83:102664. link 

57. Mohammadi K, Zhao H, Zhai M, Tung F. Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 15783-15792). link 

58. Chen KY, Chiang PH, Chou HR, Chen TW, Chang TH. Trompt: Towards a better deep neural network for tabular data. arXiv preprint arXiv:2305.18446. 2023 May 29. link 

59. Ali-Bey A, Chaib-draa B, Giguère P. Global proxy-based hard mining for visual place recognition. arXiv preprint arXiv:2302.14217. 2023 Feb 28. link 

60. Ali-Bey A, Chaib-draa B, Giguère P. Global proxy-based hard mining for visual place recognition. arXiv preprint arXiv:2302.14217. 2023 Feb 28. link 

61. Yang Z, Kafle K, Dernoncourt F, Ordonez V. Improving visual grounding by encouraging consistent gradient-based explanations. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 19165-19174). link 

62. Wang H, Joshi D, Wang S, Ji Q. Gradient-based uncertainty attribution for explainable bayesian deep learning. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 12044-12053). link 

63. Wang C, Luo X, Ross K, Li D. Vrl3: A data-driven framework for visual deep reinforcement learning. Advances in Neural Information Processing Systems. 2022 Dec 6;35:32974-88. link 

64. Ding Z, Wang J, Tu Z. Open-vocabulary universal image segmentation with maskclip. arXiv preprint arXiv:2208.08984. 2022 Aug 18. link 

65. Ben-David E, Ziser Y, Reichart R. Domain adaptation from scratch. arXiv preprint arXiv:2209.00830. 2022 Sep 2. link 

66. Zhou D, Wang N, Gao X, Han B, Wang X, Zhan Y, Liu T. Improving adversarial robustness via mutual information estimation. InInternational conference on machine learning 2022 Jun 28 (pp. 27338-27352). PMLR. link 

67. Chen Y, Jamieson K, Du S. Active multi-task representation learning. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 3271-3298). PMLR. link 

68. Crabbé J, van der Schaar M. Label-free explainability for unsupervised models. arXiv preprint arXiv:2203.01928. 2022 Mar 3. link 

69. Zhang Y, Zhang G, Khanduri P, Hong M, Chang S, Liu S. Revisiting and advancing fast adversarial training through the lens of bi-level optimization. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 26693-26712). PMLR. link 

70. Zhang M, Sohoni NS, Zhang HR, Finn C, Ré C. Correct-n-contrast: A contrastive approach for improving robustness to spurious correlations. arXiv preprint arXiv:2203.01517. 2022 Mar 3. link 

71. Yan S, Xiong X, Arnab A, Lu Z, Zhang M, Sun C, Schmid C. Multiview transformers for video recognition. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2022 (pp. 3333-3343). link 

72. Yan L, Wang Q, Cui Y, Feng F, Quan X, Zhang X, Liu D. Gl-rg: Global-local representation granularity for video captioning. arXiv preprint arXiv:2205.10706. 2022 May 22. link 

73. Diakonikolas I, Kontonis V, Tzamos C, Zarifis N. Learning general halfspaces with adversarial label noise via online gradient descent. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 5118-5141). PMLR. link 

74. Hu Q, Zhong Y, Yang T. Multi-block min-max bilevel optimization with applications in multi-task deep auc maximization. Advances in Neural Information Processing Systems. 2022 Dec 6;35:29552-65. link 

75. Frieder S, Lukasiewicz T. (Non-) Convergence Results for Predictive Coding Networks. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 6793-6810). PMLR. link 

76. Park J, Ryu EK. Exact optimal accelerated complexity for fixed-point iterations. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 17420-17457). PMLR. link 

77. Gao H, Li J, Huang H. On the convergence of local stochastic compositional gradient descent with momentum. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 7017-7035). PMLR. link 

78. Gasnikov A, Novitskii A, Novitskii V, Abdukhakimov F, Kamzolov D, Beznosikov A, Takáč M, Dvurechensky P, Gu B. The power of first-order smooth optimization for black-box non-smooth problems. arXiv preprint arXiv:2201.12289. 2022 Jan 28. link 

79. Gorbunov E, Berard H, Gidel G, Loizou N. Stochastic extragradient: General analysis and improved rates. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 7865-7901). PMLR. link 

80. Gorbunov E, Loizou N, Gidel G. Extragradient method: O (1/k) last-iterate convergence for monotone variational inequalities and connections with cocoercivity. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 366-402). PMLR. link 

81. Irons NJ, Scetbon M, Pal S, Harchaoui Z. Triangular flows for generative modeling: Statistical consistency, smoothness classes, and fast rates. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 10161-10195). PMLR. link 

82. Kim J, Yang I. Accelerated gradient methods for geodesically convex optimization: Tractable algorithms and convergence analysis. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 11255-11282). PMLR. link 

83. Kostic VR, Salzo S, Pontil M. Batch greenkhorn algorithm for entropic-regularized multimarginal optimal transport: Linear rate of convergence and iteration complexity. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 11529-11558). PMLR. link 

84. Leahy JM, Kerimkulov B, Siska D, Szpruch L. Convergence of policy gradient for entropy regularized MDPs with neural network approximation in the mean-field regime. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 12222-12252). PMLR. link 

85. Liu Y, Shang F, An W, Liu H, Lin Z. Kill a bird with two stones: Closing the convergence gaps in non-strongly convex optimization by directly accelerated SVRG with double compensation and snapshots. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 14008-14035). PMLR. link 

86. Emmenegger N, Kyng R, Zehmakan AN. On the oracle complexity of higher-order smooth non-convex finite-sum optimization. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 10718-10752). PMLR. link 

87. Kim D, Kim Y, Kwon SJ, Kang W, Moon IC. Refining generative process with discriminator guidance in score-based diffusion models. arXiv preprint arXiv:2211.17091. 2022 Nov 28. link 

88. Zilber P, Nadler B. Inductive matrix completion: No bad local minima and a fast algorithm. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 27671-27692). PMLR. link 

89. Zhu D, Li G, Wang B, Wu X, Yang T. When AUC meets DRO: Optimizing partial AUC for deep learning with non-convex convergence guarantee. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 27548-27573). PMLR. link 

90. Zhang G, Wang Y, Lessard L, Grosse RB. Near-optimal local convergence of alternating gradient descent-ascent for minimax optimization. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 7659-7679). PMLR. link 

91. Zhang G, Luo Z, Yu Y, Cui K, Lu S. Accelerating DETR convergence via semantic-aligned matching. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2022 (pp. 949-958). link 

92. Yun J, Lozano A, Yang E. Adablock: SGD with practical block diagonal matrix adaptation for deep learning. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 2574-2606). PMLR. link 

93. Shin Y. Effects of depth, width, and initialization: A convergence analysis of layer-wise training for deep linear neural networks. Analysis and Applications. 2022 Jan 31;20(01):73-119. link 

94. Bah B, Rauhut H, Terstiege U, Westdickenberg M. Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers. Information and Inference: A Journal of the IMA. 2022 Mar;11(1):307-53. link 

95. Scaman K, Malherbe C, Dos Santos L. Convergence rates of non-convex stochastic gradient descent under a generic lojasiewicz condition and local smoothness. InInternational conference on machine learning 2022 Jun 28 (pp. 19310-19327). PMLR. link 

96. Ren T, Cui F, Atsidakou A, Sanghavi S, Ho N. Towards statistical and computational complexities of Polyak step size gradient descent. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 3930-3961). PMLR. link 

97. Qiu ZH, Hu Q, Zhong Y, Zhang L, Yang T. Large-scale stochastic optimization of NDCG surrogates for deep learning with provable convergence. arXiv preprint arXiv:2202.12183. 2022 Feb 24. link 

98. El Hanchi A, Stephens D, Maddison C. Stochastic reweighted gradient descent. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 8359-8374). PMLR. link 

99. Salim A, Sun L, Richtarik P. A convergence theory for SVGD in the population limit under Talagrand’s inequality T1. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 19139-19152). PMLR. link 

100. Rangwani H, Aithal SK, Mishra M, Jain A, Radhakrishnan VB. A closer look at smoothness in domain adversarial training. InInternational conference on machine learning 2022 Jun 28 (pp. 18378-18399). PMLR. link 

101. Rakotomamonjy A, Flamary R, Salmon J, Gasso G. Convergent working set algorithm for lasso with non-convex sparse regularizers. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 5196-5211). PMLR. link 

102. Raj A, Joulani P, Gyorgy A, Szepesvári C. Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 7176-7186). PMLR. link 

103. Li H, Farnia F, Das S, Jadbabaie A. On convergence of gradient descent ascent: A tight local analysis. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 12717-12740). PMLR. link 

104. Yu Y, Lin T, Mazumdar EV, Jordan M. Fast distributionally robust learning with variance-reduced min-max optimization. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 1219-1250). PMLR. link 

105. Yang J, Orvieto A, Lucchi A, He N. Faster single-loop algorithms for minimax optimization without strong concavity. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 5485-5517). PMLR. link 

106. Yang J, Ren S. Informed learning by wide neural networks: Convergence, generalization and sampling complexity. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 25198-25240). PMLR. link 

107. Xing Y, Song Q, Cheng G. Unlabeled data help: Minimax analysis and adversarial robustness. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 136-168). PMLR. link 

108. Wu CT, Masoomi A, Gretton A, Dy J. Deep layer-wise networks have closed-form weights. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 188-225). PMLR. link 

109. Wang JK, Lin CH, Wibisono A, Hu B. Provable acceleration of heavy ball beyond quadratics for a class of Polyak-Lojasiewicz functions when the non-convexity is averaged-out. InInternational conference on machine learning 2022 Jun 28 (pp. 22839-22864). PMLR. link 

110. Wang H, Si H, Li B, Zhao H. Provable domain generalization via invariant-feature subspace recovery. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 23018-23033). PMLR. link 

111. Wang B, Yang T. Finite-sum coupled compositional stochastic optimization: Theory and applications. arXiv preprint arXiv:2202.12396. 2022 Feb 24. link 

112. Cohen E, Hallak N, Teboulle M. A dynamic alternating direction of multipliers for nonconvex minimization with nonlinear functional equality constraints. Journal of Optimization Theory and Applications. 2022 Jun 1:1-30. link 

113. Chamon LF, Paternain S, Calvo-Fullana M, Ribeiro A. Constrained learning with non-convex losses. IEEE Transactions on Information Theory. 2022 Jul 1;69(3):1739-60. link 

114. Horváth S, Lei L, Richtárik P, Jordan MI. Adaptivity of stochastic gradient methods for nonconvex optimization. SIAM Journal on Mathematics of Data Science. 2022;4(2):634-48. link 

115. Vigogna S, Meanti G, De Vito E, Rosasco L. Multiclass learning with margin: exponential rates with no bias-variance trade-off. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 22260-22269). PMLR. link 

116. Vaswani S, Dubois-Taine B, Babanezhad R. Towards noise-adaptive, problem-adaptive (accelerated) stochastic gradient descent. InInternational conference on machine learning 2022 Jun 28 (pp. 22015-22059). PMLR. link 

117. Tran TH, Scheinberg K, Nguyen LM. Nesterov accelerated shuffling gradient method for convex optimization. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 21703-21732). PMLR. link 

118. Sebbouh O, Cuturi M, Peyré G. Randomized stochastic gradient descent ascent. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 2941-2969). PMLR. link 

119. Loizou N, Vaswani S, Laradji IH, Lacoste-Julien S. Stochastic polyak step-size for sgd: An adaptive learning rate for fast convergence. InInternational Conference on Artificial Intelligence and Statistics 2021 Mar 18 (pp. 1306-1314). PMLR. link 

120. Asi H, Duchi J, Fallah A, Javidbakht O, Talwar K. Private adaptive gradient methods for convex optimization. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 383-392). PMLR. link 

121. Chen T, Sun Y, Yin W. Solving stochastic compositional optimization is nearly as easy as solving stochastic optimization. IEEE Transactions on Signal Processing. 2021 Jun 25;69:4937-48. link 

122. Jin C, Netrapalli P, Ge R, Kakade SM, Jordan MI. On nonconvex optimization for machine learning: Gradients, stochasticity, and saddle points. Journal of the ACM (JACM). 2021 Feb 24;68(2):1-29. link 

123. Diakonikolas J, Daskalakis C, Jordan MI. Efficient methods for structured nonconvex-nonconcave min-max optimization. InInternational Conference on Artificial Intelligence and Statistics 2021 Mar 18 (pp. 2746-2754). PMLR. link 

124. Amir I, Koren T, Livni R. SGD generalizes better than GD (and regularization doesn’t help). InConference on Learning Theory 2021 Jul 21 (pp. 63-92). PMLR. link 

125. Vlaski S, Sayed AH. Distributed learning in non-convex environments—Part I: Agreement at a linear rate. IEEE Transactions on Signal Processing. 2021 Jan 12;69:1242-56. link 

126. Vlaski S, Sayed AH. Distributed learning in non-convex environments—Part II: Polynomial escape from saddle-points. IEEE Transactions on Signal Processing. 2021 Jan 13;69:1257-70. link 

127. Bu Z, Xu S, Chen K. A dynamical view on optimization algorithms of overparameterized neural networks. InInternational conference on artificial intelligence and statistics 2021 Mar 18 (pp. 3187-3195). PMLR. link 

128. Farnia F, Ozdaglar A. Train simultaneously, generalize better: Stability of gradient-based minimax learners. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 3174-3185). PMLR. link 

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From: UK Biobank publications 

1. Kany S, Rämö JT, Playford D, Strange G, Hou C, Jurgens SJ, Nauffal V, Cunningham JW, Lau ES, Butte AJ, Ho JE. New threshold for defining mild aortic stenosis derived from velocity-encoded MRI in 60,000 individuals. Journal of the American College of Cardiology. 2025 Apr 8;85(13):1387-99. link 

2. Oikonomou EK, Holste G, Yuan N, Coppi A, McNamara RL, Haynes NA, Vora AN, Velazquez EJ, Li F, Menon V, Kapadia SR. A multimodal video-based AI biomarker for aortic stenosis development and progression. JAMA cardiology. 2024 Jun 1;9(6):534-44. link 

3. Thomson RJ, Grafton‐Clarke C, Matthews G, Swoboda PP, Swift AJ, Frangi A, Petersen SE, Aung N, Garg P. Risk factors for raised left ventricular filling pressure by cardiovascular magnetic resonance: Prognostic insights. ESC Heart Failure. 2024 Dec;11(6):4148-59. link 

4. Muffoletto M, Xu H, Burns R, Suinesiaputra A, Nasopoulou A, Kunze KP, Neji R, Petersen SE, Niederer SA, Rueckert D, Young AA. Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short-and long-axis analyses in UK Biobank. European Heart Journal-Cardiovascular Imaging. 2024 Oct;25(10):1374-83. link 

5. Chadalavada S, Fung K, Rauseo E, Lee AM, Khanji MY, Amir-Khalili A, Paiva J, Naderi H, Banik S, Chirvasa M, Jensen MT. Myocardial strain measured by cardiac magnetic resonance predicts cardiovascular morbidity and death. Journal of the American College of Cardiology. 2024 Aug 13;84(7):648-59. link

6. Bertrand A, Lewis A, Camps J, Grau V, Rodriguez B. Multi-modal characterisation of early-stage, subclinical cardiac deterioration in patients with type 2 diabetes. Cardiovascular Diabetology. 2024 Oct 19;23(1):371. link 

7. Linge J, Widholm P, Nilsson D, Kugelberg A, Olbers T, Leinhard OD. Risk stratification using magnetic resonance imaging-derived, personalized z-scores of visceral adipose tissue, subcutaneous adipose tissue, and liver fat in persons with obesity. Surgery for Obesity and Related Diseases. 2024 May 1;20(5):419-24. link 

8. Li L, Camps J, Wang ZJ, Beetz M, Banerjee A, Rodriguez B, Grau V. Toward enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference. IEEE transactions on medical imaging. 2024 Feb 19;43(7):2466-78. link 

9. Lang O, Yaya-Stupp D, Traynis I, Cole-Lewis H, Bennett CR, Lyles CR, Lau C, Irani M, Semturs C, Webster DR, Corrado GS. Using generative AI to investigate medical imagery models and datasets. EBioMedicine. 2024 Apr 1;102. link 

10. Li Y, Chan E, Puyol-Antón E, Ruijsink B, Cecelja M, King AP, Razavi R, Chowienczyk P. Hemodynamic determinants of elevated blood pressure and hypertension in the middle to older-age UK population: a UK Biobank Imaging Study. Hypertension. 2023 Nov;80(11):2473-84. link 

11. Dawood T, Chen C, Sidhu BS, Ruijsink B, Gould J, Porter B, Elliott MK, Mehta V, Rinaldi CA, Puyol-Antón E, Razavi R. Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images. Medical Image Analysis. 2023 Aug 1;88:102861. link 

12.  Raisi-Estabragh Z, McCracken C, Hann E, Condurache DG, Harvey NC, Munroe PB, Ferreira VM, Neubauer S, Piechnik SK, Petersen SE. Incident clinical and mortality associations of myocardial native T1 in the UK Biobank. Cardiovascular Imaging. 2023 Apr 1;16(4):450-60. link 

13. Pujadas ER, Raisi-Estabragh Z, Szabo L, McCracken C, Morcillo CI, Campello VM, Martín-Isla C, Atehortua AM, Vago H, Merkely B, Maurovich-Horvat P. Prediction of incident cardiovascular events using machine learning and CMR radiomics. European radiology. 2023 May;33(5):3488-500. link 

14. Raisi-Estabragh Z, McCracken C, Condurache D, Aung N, Vargas JD, Naderi H, Munroe PB, Neubauer S, Harvey NC, Petersen SE. Left atrial structure and function are associated with cardiovascular outcomes independent of left ventricular measures: a UK Biobank CMR study. European Heart Journal-Cardiovascular Imaging. 2022 Sep 1;23(9):1191-200. link 

15. Pujadas ER, Raisi-Estabragh Z, Szabo L, Morcillo CI, Campello VM, Martin-Isla C, Vago H, Merkely B, Harvey NC, Petersen SE, Lekadir K. Atrial fibrillation prediction by combining ECG markers and CMR radiomics. Scientific Reports. 2022 Nov 7;12(1):18876. link 

16. Cecelja M, Ruijsink B, Puyol‐Antón E, Li Y, Godwin H, King AP, Razavi R, Chowienczyk P. Aortic distensibility measured by automated analysis of magnetic resonance imaging predicts adverse cardiovascular events in UK biobank. Journal of the American Heart Association. 2022 Dec 6;11(23):e026361. link 

17. Puyol-Antón E, Sidhu BS, Gould J, Porter B, Elliott MK, Mehta V, Rinaldi CA, King AP. A multimodal deep learning model for cardiac resynchronisation therapy response prediction. Medical Image Analysis. 2022 Jul 1;79:102465. link 

18. Ardissino M, McCracken C, Bard A, Antoniades C, Neubauer S, Harvey NC, Petersen SE, Raisi-Estabragh Z. Pericardial adiposity is independently linked to adverse cardiovascular phenotypes: a CMR study of 42 598 UK Biobank participants. European Heart Journal-Cardiovascular Imaging. 2022 Nov 1;23(11):1471-81. link 

19. Pirruccello JP, Lin H, Khurshid S, Nekoui M, Weng LC, Vasan RS, Isselbacher EM, Benjamin EJ, Lubitz SA, Lindsay ME, Ellinor PT. Development of a prediction model for ascending aortic diameter among asymptomatic individuals. JAMA. 2022 Nov 15;328(19):1935-44. link 

20. Khurshid S, Friedman S, Pirruccello JP, Di Achille P, Diamant N, Anderson CD, Ellinor PT, Batra P, Ho JE, Philippakis AA, Lubitz SA. Deep learning to predict cardiac magnetic resonance–derived left ventricular mass and hypertrophy from 12-lead ECGs. Circulation: Cardiovascular Imaging. 2021 Jun;14(6):e012281. link 

21. Puyol-Antón E, Ruijsink B, Baumgartner CF, Masci PG, Sinclair M, Konukoglu E, Razavi R, King AP. Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with uncertainty-based quality-control. Journal of Cardiovascular Magnetic Resonance. 2020 Jan 20;22(1):60. link 

22. Ruijsink B, Puyol-Antón E, Oksuz I, Sinclair M, Bai W, Schnabel JA, Razavi R, King AP. Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function. Cardiovascular Imaging. 2020 Mar 1;13(3):684-95. link 

23. Guo F, Ng M, Goubran M, Petersen SE, Piechnik SK, Neubauer S, Wright G. Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach. Medical image analysis. 2020 Apr 1;61:101636. link 

24. Honigberg MC, Pirruccello JP, Aragam K, Sarma AA, Scott NS, Wood MJ, Natarajan P. Menopausal age and left ventricular remodeling by cardiac magnetic resonance imaging among 14,550 women. American heart journal. 2020 Nov 1;229:138-43. link 

25. Bai W, Suzuki H, Huang J, Francis C, Wang S, Tarroni G, Guitton F, Aung N, Fung K, Petersen SE, Piechnik SK. A population-based phenome-wide association study of cardiac and aortic structure and function. Nature medicine. 2020 Oct;26(10):1654-62. link 

26. Córdova-Palomera A, Tcheandjieu C, Fries JA, Varma P, Chen VS, Fiterau M, Xiao K, Tejeda H, Keavney BD, Cordell HJ, Tanigawa Y. Cardiac imaging of aortic valve area from 34 287 UK Biobank participants reveals novel genetic associations and shared genetic comorbidity with multiple disease phenotypes. Circulation: Genomic and Precision Medicine. 2020 Dec;13(6):e003014. link 

27. Pirruccello JP, Bick A, Wang M, Chaffin M, Friedman S, Yao J, Guo X, Venkatesh BA, Taylor KD, Post WS, Rich S. Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy. Nature communications. 2020 May 7;11(1):2254. link 

28. Fung K, Cheshire C, Cooper JA, Catarino P, Piechnik SK, Neubauer S, Bhagra S, Pettit S, Petersen SE. Validation of cardiovascular magnetic resonance–derived equation for predicted left ventricular mass using the UK Biobank Imaging Cohort: tool for donor-recipient size matching. Circulation: Heart Failure. 2019 Dec;12(12):e006362. link 

29. Fries JA, Varma P, Chen VS, Xiao K, Tejeda H, Saha P, Dunnmon J, Chubb H, Maskatia S, Fiterau M, Delp S. Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences. Nature communications. 2019 Jul 15;10(1):3111. link 

30. Suinesiaputra A, Sanghvi MM, Aung N, Paiva JM, Zemrak F, Fung K, Lukaschuk E, Lee AM, Carapella V, Kim YJ, Francis J. Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results. The international journal of cardiovascular imaging. 2018 Feb;34(2):281-91. link 

31. Sanghvi MM, Aung N, Cooper JA, Paiva JM, Lee AM, Zemrak F, Fung K, Thomson RJ, Lukaschuk E, Carapella V, Kim YJ. The impact of menopausal hormone therapy (MHT) on cardiac structure and function: insights from the UK Biobank imaging enhancement study. PloS one. 2018 Mar 8;13(3):e0194015. link 

32. Puyol-Anton E, Sinclair M, Gerber B, Amzulescu MS, Langet H, De Craene M, Aljabar P, Piro P, King AP. A multimodal spatiotemporal cardiac motion atlas from MR and ultrasound data. Medical image analysis. 2017 Aug 1;40:96-110. link 

From: NIH NCI CDAS 

1. Rathore S, Gautam A, Raghav P, Subramaniam V, Gupta V, Rathore M, Rathore A, Rathore S, Iyengar S. Fully automated coronary artery calcium score and risk categorization from chest CT using deep learning and multiorgan segmentation: A validation study from National Lung Screening Trial (NLST). IJC Heart & Vasculature. 2025 Feb 1;56:101593. link 

2. Gendarme S, Irajizad E, Long JP, Fahrmann JF, Dennison JB, Ghasemi SM, Dou R, Volk RJ, Meza R, Toumazis I, Canoui-Poitrine F. Impact of comorbidities on the mortality benefits of lung cancer screening: a post-hoc analysis of the PLCO and NLST trials. Journal of Thoracic Oncology. 2025 May 1;20(5):565-76. link 

3. Jiang Y, Ebrahimpour L, Després P, Manem VS. A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort. Scientific reports. 2025 Jan 11;15(1):1736. link 

4. Meza R, ten Haaf K, Kong CY, Erdogan A, Black WC, Tammemagi MC, Choi SE, Jeon J, Han SS, Munshi V, van Rosmalen J. Comparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials. Cancer. 2014 Jun 1;120(11):1713-24. link 

5. Oo DW, Sturniolo A, Jung M, Langenbach M, Foldyna B, Kiel DP, Aerts HJ, Natarajan P, Lu MT, Raghu VK. Opportunistic Assessment Of Cardiovascular Risk Using Ai-Derived Structural Aortic And Cardiac Phenotypes From Non-Contrast Chest Computed Tomography. medRxiv. 2025 Jan 29. link 

6. Langenbach IL, Hadzic I, Zeleznik R, Langenbach MC, Maintz D, Mayrhofer T, Lu MT, Aerts HJ, Foldyna B. Association of epicardial adipose tissue changes on serial chest CT scans with mortality: insights from the national lung screening trial. Radiology. 2025 Feb 18;314(2):e240473. link 

7. Beeche C, Yu T, Wang J, Wilson D, Chen P, Duman E, Pu J. A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy. British Journal of Radiology. 2025 Mar;98(1167):412-21. link 

8. Jiang Y, Manem VS. Data augmented lung cancer prediction framework using the nested case control NLST cohort. Frontiers in oncology. 2025 Feb 25;15:1492758. link 

9. Jang S, Kim J, Lee S, Kim YW, Kim J, Lee KW, Lee CT. Visual Emphysema as a Category Modifier in Lung-RADS: Secondary Analysis of National Lung Screening Trial. Journal of the American College of Radiology. 2025 Mar 4. link 

10. Tailor TD, Gutman R, An N, Hoffman RM, Chiles C, Carlos RC, Sicks JD, Gareen IF. Positive Screens Are More Likely in a National Lung Cancer Screening Registry Than the National Lung Screening Trial. Journal of the American College of Radiology. 2025 Feb 27. link 

11. Lin F, Zhang Z, Wang J, Liang C, Xu J, Zeng X, Zeng Q, Chen H, Zhuang J, Ma Y, Ma Q. AutoCOPD–A novel and practical machine learning model for COPD detection using whole-lung inspiratory quantitative CT measurements: a retrospective, multicenter study. EClinicalMedicine. 2025 Apr 1;82. link 

12. Behr CM, IJzerman MJ, Kip MM, Groen HJ, Heuvelmans MA, van den Berge M, van der Harst P, Vonder M, Vliegenthart R, Koffijberg H. Model-Based Cost-Utility Analysis of Combined Low-Dose Computed Tomography Screening for Lung Cancer, Chronic Obstructive Pulmonary Disease, and Cardiovascular Disease. JTO clinical and research reports. 2025 Feb 19;6(5):100813. link 

13. Wang JM, Bose S, Murray S, Labaki WW, Kazerooni EA, Chung JH, Flaherty KR, Han MK, Hatt CR, Oldham JM. Quantitative CT Measures of Lung Fibrosis and Outcomes in the National Lung Screening Trial. Annals of the American Thoracic Society. 2025 Apr 11(ja). link 

14. Wang X, Sharpnack J, Lee TC. Improving lung cancer diagnosis and survival prediction with deep learning and CT imaging. PLoS One. 2025 Jun 11;20(6):e0323174. link 

15. Krishnaswamy D, Bontempi D, Thiriveedhi VK, Punzo D, Clunie D, Bridge CP, Aerts HJ, Kikinis R, Fedorov A. Enrichment of lung cancer computed tomography collections with AI-derived annotations. Scientific data. 2024 Jan 4;11(1):25. link 

16. Chen JR, Hou KY, Wang YC, Lin SP, Mo YH, Peng SC, Lu CF. Enhanced Malignancy Prediction of Small Lung Nodules in Different Populations Using Transfer Learning on Low-Dose Computed Tomography. Diagnostics. 2025 Jun 8;15(12):1460. link 

17. Xie Y, Zhang Y, Zhang P, Li Y, Xu B, Shao F, Zhang Y, Yang T, Li J, Li C, Chen T. Timing of screening benefit for lung cancer with low-dose computed tomography. Chest. 2025 Jun 18. link 

18. Sun Y, Kang J, Haridas C, Mayne N, Potter A, Yang CF, Christiani DC, Li Y. Penalized deep partially linear cox models with application to CT scans of lung cancer patients. Biometrics. 2024 Mar;80(1):ujad024. link 

19. Krishnan AR, Xu K, Li TZ, Remedios LW, Sandler KL, Maldonado F, Landman BA. Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks. Medical Physics. 2024 Aug;51(8):5510-23. link 

20. Foldyna B, Hadzic I, Zeleznik R, Langenbach MC, Raghu VK, Mayrhofer T, Lu MT, Aerts HJ. Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers. Communications medicine. 2024 Mar 13;4(1):44. link 

21. Liu J, Qi L, Xu Q, Chen J, Cui S, Li F, Wang Y, Cheng S, Tan W, Zhou Z, Wang J. A self-supervised learning-based fine-grained classification model for distinguishing malignant from benign subcentimeter solid pulmonary nodules. Academic Radiology. 2024 Nov 1;31(11):4687-95. link 

22. Wang Y, Zhou C, Ying L, Lee E, Chan HP, Chughtai A, Hadjiiski LM, Kazerooni EA. Leveraging serial low-dose CT scans in radiomics-based reinforcement learning to improve early diagnosis of lung cancer at baseline screening. Radiology: Cardiothoracic Imaging. 2024 May 16;6(3):e230196. link 

23. Thiriveedhi VK, Krishnaswamy D, Clunie D, Pieper S, Kikinis R, Fedorov A. Cloud-based large-scale curation of medical imaging data using AI segmentation. Research Square. 2024 May 3:rs-3. link 

24. Hermoza R, Nascimento JC, Carneiro G. Weakly-supervised preclinical tumor localization associated with survival prediction from lung cancer screening Chest X-ray images. Computerized Medical Imaging and Graphics. 2024 Jul 1;115:102395. link 

25. Wang Z, Sui X, Song W, Xue F, Han W, Hu Y, Jiang J. Reinforcement learning for individualized lung cancer screening schedules: A nested case–control study. Cancer Medicine. 2024 Jul;13(13):e7436. link 

26. Yang R, Li W, Yu S, Wu Z, Zhang H, Liu X, Tao L, Li X, Huang J, Guo X. Deep learning model for pathological grading and prognostic assessment of lung cancer using CT imaging: a study on NLST and external validation cohorts. Academic Radiology. 2025 Jan 1;32(1):533-42. link 

27. Mascalchi M, Puliti D, Cavigli E, Cortés-Ibáñez FO, Picozzi G, Carrozzi L, Gorini G, Delorme S, Zompatori M, De Luca GR, Diciotti S. Large cell carcinoma of the lung: LDCT features and survival in screen-detected cases. European journal of radiology. 2024 Oct 1;179:111679. link 

28. Marcinkiewicz AM, Buchwald M, Shanbhag A, Bednarski BP, Killekar A, Miller RJ, Builoff V, Lemley M, Berman DS, Dey D, Slomka PJ. AI for multistructure incidental findings and mortality prediction at chest CT in lung cancer screening. Radiology. 2024 Sep 17;312(3):e240541. link 

29. Mascalchi M, Cavigli E, Picozzi G, Cozzi D, De Luca GR, Diciotti S. The Azygos Esophageal Recess Is Not to Be Missed in Screening Lung Cancer With LDCT. Journal of Thoracic Imaging. 2025 May 1;40(3):e0813. link 

30. Aslani S, Alluri P, Gudmundsson E, Chandy E, McCabe J, Devaraj A, Horst C, Janes SM, Chakkara R, Alexander DC, Nair A. Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning. Computerized Medical Imaging and Graphics. 2024 Sep 1;116:102399. link 

31. Ebrahimpour L, Després P, Manem VS. Differential Radiomics‐Based Signature Predicts Lung Cancer Risk Accounting for Imaging Parameters in NLST Cohort. Cancer Medicine. 2024 Oct;13(20):e70359. link 

32. Simon J, Mikhael P, Graur A, Chang AE, Skates SJ, Osarogiagbon RU, Sequist LV, Fintelmann FJ. Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm. Investigative Radiology. 2024 Dec 13:10-97. link 

33. Pan Z, Zhang R, Shen S, Lin Y, Zhang L, Wang X, Ye Q, Wang X, Chen J, Zhao Y, Christiani DC. OWL: an optimized and independently validated machine learning prediction model for lung cancer screening based on the UK Biobank, PLCO, and NLST populations. EBioMedicine. 2023 Feb 1;88. link 

34. Liu P, Ji L, Ye F, Fu B. GraphLSurv: A scalable survival prediction network with adaptive and sparse structure learning for histopathological whole-slide images. Computer methods and programs in biomedicine. 2023 Apr 1;231:107433. link 

35. Mikhael PG, Wohlwend J, Yala A, Karstens L, Xiang J, Takigami AK, Bourgouin PP, Chan P, Mrah S, Amayri W, Juan YH. Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. Journal of Clinical Oncology. 2023 Apr 20;41(12):2191-200. link 

36. Huang YS, Wang TC, Huang SZ, Zhang J, Chen HM, Chang YC, Chang RF. An improved 3-D attention CNN with hybrid loss and feature fusion for pulmonary nodule classification. Computer Methods and Programs in Biomedicine. 2023 Feb 1;229:107278. link 

37. Landy R, Wang VL, Baldwin DR, Pinsky PF, Cheung LC, Castle PE, Skarzynski M, Robbins HA, Katki HA. Recalibration of a deep learning model for low-dose computed tomographic images to inform lung cancer screening intervals. JAMA Network Open. 2023 Mar 1;6(3):e233273-. link 

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YOLO series

• Lei M, Li S, Wu Y, Hu H, Zhou Y, Zheng X, Ding G, Du S, Wu Z, Gao Y. YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception. arXiv preprint arXiv:2506.17733. 2025 Jun 21. link 

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• Khanam R, Hussain M. Yolov11: An overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725. 2024 Oct 23. link 

• Wang A, Chen H, Liu L, Chen K, Lin Z, Han J. Yolov10: Real-time end-to-end object detection. Advances in Neural Information Processing Systems. 2024 Dec 16;37:107984-8011. link 

• Wang CY, Yeh IH, Mark Liao HY. Yolov9: Learning what you want to learn using programmable gradient information. InEuropean conference on computer vision 2024 Sep 29 (pp. 1-21). Cham: Springer Nature Switzerland. link 

• What is YOLOv8? A Complete Guide.

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VQ-VAE series

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InternVideo series

• Wang Y, Li K, Li X, Yu J, He Y, Chen G, Pei B, Zheng R, Wang Z, Shi Y, Jiang T. Internvideo2: Scaling foundation models for multimodal video understanding. InEuropean Conference on Computer Vision 2024 Sep 29 (pp. 396-416). Cham: Springer Nature Switzerland. link 

• Wang Y, Li K, Li Y, He Y, Huang B, Zhao Z, Zhang H, Xu J, Liu Y, Wang Z, Xing S. Internvideo: General video foundation models via generative and discriminative learning. arXiv preprint arXiv:2212.03191. 2022 Dec 6. link 

InternVL series

• Zhu J, Wang W, Chen Z, Liu Z, Ye S, Gu L, Tian H, Duan Y, Su W, Shao J, Gao Z. Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479. 2025 Apr 14. link 

• Chen Z, Wang W, Cao Y, Liu Y, Gao Z, Cui E, Zhu J, Ye S, Tian H, Liu Z, Gu L. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271. 2024 Dec 6. link 

• Chen Z, Wu J, Wang W, Su W, Chen G, Xing S, Zhong M, Zhang Q, Zhu X, Lu L, Li B. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2024 (pp. 24185-24198). link 

V-JEPA series

• Assran M, Bardes A, Fan D, Garrido Q, Howes R, Muckley M, Rizvi A, Roberts C, Sinha K, Zholus A, Arnaud S. V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning. arXiv preprint arXiv:2506.09985. 2025 Jun 11. link 

• Bardes A, Garrido Q, Ponce J, Chen X, Rabbat M, LeCun Y, Assran M, Ballas N. Revisiting feature prediction for learning visual representations from video. arXiv preprint arXiv:2404.08471. 2024 Feb 15. link 

Uniperceiver series

• Li H, Zhu J, Jiang X, Zhu X, Li H, Yuan C, Wang X, Qiao Y, Wang X, Wang W, Dai J. Uni-perceiver v2: A generalist model for large-scale vision and vision-language tasks. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 2691-2700). link 

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Uniformer series

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• Li K, Wang Y, Gao P, Song G, Liu Y, Li H, Qiao Y. Uniformer: Unified transformer for efficient spatiotemporal representation learning. arXiv preprint arXiv:2201.04676. 2022 Jan 12. link 

Unified IO series

• Lu J, Clark C, Lee S, Zhang Z, Khosla S, Marten R, Hoiem D, Kembhavi A. Unified-io 2: Scaling autoregressive multimodal models with vision language audio and action. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (pp. 26439-26455). link 

• Lu J, Clark C, Zellers R, Mottaghi R, Kembhavi A. Unified-io: A unified model for vision, language, and multi-modal tasks. arXiv preprint arXiv:2206.08916. 2022 Jun 17. link 

Unbiased teacher series

• Liu YC, Ma CY, Kira Z. Unbiased teacher v2: Semi-supervised object detection for anchor-free and anchor-based detectors. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 (pp. 9819-9828). link 

• Liu YC, Ma CY, He Z, Kuo CW, Chen K, Zhang P, Wu B, Kira Z, Vajda P. Unbiased teacher for semi-supervised object detection. arXiv preprint arXiv:2102.09480. 2021 Feb 18. link 

Swin Transformer series

• Liu Z, Hu H, Lin Y, Yao Z, Xie Z, Wei Y, Ning J, Cao Y, Zhang Z, Dong L, Wei F. Swin transformer v2: Scaling up capacity and resolution. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2022 (pp. 12009-12019). link 

• Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B. Swin transformer: Hierarchical vision transformer using shifted windows. InProceedings of the IEEE/CVF international conference on computer vision 2021 (pp. 10012-10022). link 

ShuffleNet series

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• Zhang X, Zhou X, Lin M, Sun J. Shufflenet: An extremely efficient convolutional neural network for mobile devices. InProceedings of the IEEE conference on computer vision and pattern recognition 2018 (pp. 6848-6856). link 

SAM series

• Ravi N, Gabeur V, Hu YT, Hu R, Ryali C, Ma T, Khedr H, Rädle R, Rolland C, Gustafson L, Mintun E. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714. 2024 Aug 1. link 

• Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo WY, Dollár P. Segment anything. InProceedings of the IEEE/CVF international conference on computer vision 2023 (pp. 4015-4026). link 

RepPoints series

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• Yang Z, Liu S, Hu H, Wang L, Lin S. Reppoints: Point set representation for object detection. InProceedings of the IEEE/CVF international conference on computer vision 2019 (pp. 9657-9666). link 

R-CNN series 

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• Ren S, He K, Girshick R, Sun J. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems. 2015;28. link 

• Girshick R. Fast r-cnn. InProceedings of the IEEE international conference on computer vision 2015 (pp. 1440-1448). link 

• Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. InProceedings of the IEEE conference on computer vision and pattern recognition 2014 (pp. 580-587). link 

PVT series

• Wang W, Xie E, Li X, Fan DP, Song K, Liang D, Lu T, Luo P, Shao L. Pvt v2: Improved baselines with pyramid vision transformer. Computational visual media. 2022 Sep;8(3):415-24. link 

• Wang W, Xie E, Li X, Fan DP, Song K, Liang D, Lu T, Luo P, Shao L. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. InProceedings of the IEEE/CVF international conference on computer vision 2021 (pp. 568-578). link 

PALM series

• Anil R, Dai AM, Firat O, Johnson M, Lepikhin D, Passos A, Shakeri S, Taropa E, Bailey P, Chen Z, Chu E. Palm 2 technical report. arXiv preprint arXiv:2305.10403. 2023 May 17. link 

• Chowdhery A, Narang S, Devlin J, Bosma M, Mishra G, Roberts A, Barham P, Chung HW, Sutton C, Gehrmann S, Schuh P. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research. 2023;24(240):1-13.

Multiscale vision transformer series

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• Fan H, Xiong B, Mangalam K, Li Y, Yan Z, Malik J, Feichtenhofer C. Multiscale vision transformers. InProceedings of the IEEE/CVF international conference on computer vision 2021 (pp. 6824-6835). link 

MOTR series

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• Zhang Y, Wang T, Zhang X. Motrv2: Bootstrapping end-to-end multi-object tracking by pretrained object detectors. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2023 (pp. 22056-22065). link 

• Zeng F, Dong B, Zhang Y, Wang T, Zhang X, Wei Y. Motr: End-to-end multiple-object tracking with transformer. InEuropean conference on computer vision 2022 Oct 23 (pp. 659-675). Cham: Springer Nature Switzerland. link 

MoCo series

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• Chen X, Fan H, Girshick R, He K. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297. 2020 Mar 9. link 

• He K, Fan H, Wu Y, Xie S, Girshick R. Momentum contrast for unsupervised visual representation learning. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2020 (pp. 9729-9738). link 

MobileNet series

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• Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V, Le QV. Searching for mobilenetv3. InProceedings of the IEEE/CVF international conference on computer vision 2019 (pp. 1314-1324). link 

• Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. Mobilenetv2: Inverted residuals and linear bottlenecks. InProceedings of the IEEE conference on computer vision and pattern recognition 2018 (pp. 4510-4520). link 

• Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. 2017 Apr 17. link 

Llama series

• The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation.

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• Touvron H, Martin L, Stone K, Albert P, Almahairi A, Babaei Y, Bashlykov N, Batra S, Bhargava P, Bhosale S, Bikel D. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288. 2023 Jul 18. link 

• Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, Rozière B, Goyal N, Hambro E, Azhar F, Rodriguez A. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971. 2023 Feb 27. link 

InceptionNet series

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• Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. InProceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 2818-2826). link 

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GPT series

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• Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A, Schulman J. Training language models to follow instructions with human feedback. Advances in neural information processing systems. 2022 Dec 6;35:27730-44. link 

• Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S. Language models are few-shot learners. Advances in neural information processing systems. 2020;33:1877-901. link 

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Generalized focal loss series

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• Li X, Wang W, Wu L, Chen S, Hu X, Li J, Tang J, Yang J. Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in neural information processing systems. 2020;33:21002-12. link 

ExpansionNet series

• Hu JC. Expansionnet: Exploring the sequence length bottleneck in the transformer for image captioning. CoRR. 2022 Jan 1. link 

• Hu JC, Cavicchioli R, Capotondi A. Exploiting multiple sequence lengths in fast end to end training for image captioning. In2023 IEEE International Conference on Big Data (BigData) 2023 Dec 15 (pp. 2173-2182). IEEE. link 

EfficientNet series

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• Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. InInternational conference on machine learning 2019 May 24 (pp. 6105-6114). PMLR. link 

Florence series

• Xiao B, Wu H, Xu W, Dai X, Hu H, Lu Y, Zeng M, Liu C, Yuan L. Florence-2: Advancing a unified representation for a variety of vision tasks. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (pp. 4818-4829). link 

• Yuan L, Chen D, Chen YL, Codella N, Dai X, Gao J, Hu H, Huang X, Li B, Li C, Liu C. Florence: A new foundation model for computer vision. arXiv preprint arXiv:2111.11432. 2021 Nov 22. link 

Detclip series

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• Yao L, Han J, Liang X, Xu D, Zhang W, Li Z, Xu H. Detclipv2: Scalable open-vocabulary object detection pre-training via word-region alignment. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 23497-23506). link 

• Yao L, Han J, Wen Y, Liang X, Xu D, Zhang W, Li Z, Xu C, Xu H. Detclip: Dictionary-enriched visual-concept paralleled pre-training for open-world detection. Advances in Neural Information Processing Systems. 2022 Dec 6;35:9125-38. link 

Deformable convolutional network series

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• Li H, Zhang Y, Zhang Y, Li H, Sang L. Dcnv3: Towards next generation deep cross network for ctr prediction. arXiv e-prints. 2024 Jul:arXiv-2407. link 

• Zhu X, Hu H, Lin S, Dai J. Deformable convnets v2: More deformable, better results. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2019 (pp. 9308-9316). link 

• Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y. Deformable convolutional networks. InProceedings of the IEEE international conference on computer vision 2017 (pp. 764-773). link 

Deepseek series

• Liu A, Feng B, Xue B, Wang B, Wu B, Lu C, Zhao C, Deng C, Zhang C, Ruan C, Dai D. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437. 2024 Dec 27. link 

• Liu A, Feng B, Wang B, Wang B, Liu B, Zhao C, Dengr C, Ruan C, Dai D, Guo D, Yang D. Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model. arXiv preprint arXiv:2405.04434. 2024 May 7. link 

• Xin H, Ren ZZ, Song J, Shao Z, Zhao W, Wang H, Liu B, Zhang L, Lu X, Du Q, Gao W. Deepseek-prover-v1. 5: Harnessing proof assistant feedback for reinforcement learning and monte-carlo tree search. arXiv preprint arXiv:2408.08152. 2024 Aug 15. link 

• Bi X, Chen D, Chen G, Chen S, Dai D, Deng C, Ding H, Dong K, Du Q, Fu Z, Gao H. Deepseek llm: Scaling open-source language models with longtermism. arXiv preprint arXiv:2401.02954. 2024 Jan 5. link 

DALL E series

• What's new with DALL·E 3?

• Ramesh A, Dhariwal P, Nichol A, Chu C, Chen M. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125. 2022 Apr 13;1(2):3. link 

• Ramesh A, Pavlov M, Goh G, Gray S, Voss C, Radford A, Chen M, Sutskever I. Zero-shot text-to-image generation. InInternational conference on machine learning 2021 Jul 1 (pp. 8821-8831). Pmlr. link 

ConvNext series

• Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, Xie S. Convnext v2: Co-designing and scaling convnets with masked autoencoders. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2023 (pp. 16133-16142). link 

• Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, Xie S. A convnet for the 2020s. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2022 (pp. 11976-11986). link 

BLIP series

• Xue L, Shu M, Awadalla A, Wang J, Yan A, Purushwalkam S, Zhou H, Prabhu V, Dai Y, Ryoo MS, Kendre S. xgen-mm (blip-3): A family of open large multimodal models. arXiv preprint arXiv:2408.08872. 2024 Aug 16. link 

• Li J, Li D, Savarese S, Hoi S. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. InInternational conference on machine learning 2023 Jul 3 (pp. 19730-19742). PMLR. link 

• Li J, Li D, Xiong C, Hoi S. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. InInternational conference on machine learning 2022 Jun 28 (pp. 12888-12900). PMLR. link 

BEIT series

• Wang W, Bao H, Dong L, Bjorck J, Peng Z, Liu Q, Aggarwal K, Mohammed OK, Singhal S, Som S, Wei F. Image as a foreign language: Beit pretraining for vision and vision-language tasks. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 19175-19186). link 

• Peng Z, Dong L, Bao H, Ye Q, Wei F. Beit v2: Masked image modeling with vector-quantized visual tokenizers. arXiv preprint arXiv:2208.06366. 2022 Aug 12. link 

• Bao H, Dong L, Piao S, Wei F. Beit: Bert pre-training of image transformers. arXiv preprint arXiv:2106.08254. 2021 Jun 15. link