Liboni LH, Budzinski RC, Busch AN, Löwe S, Keller TA, Welling M, Muller LE. Image segmentation with traveling waves in an exactly solvable recurrent neural network. Proceedings of the National Academy of Sciences. 2025 Jan 7;122(1):e2321319121. link
Weaver DT, King ES, Maltas J, Scott JG. Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance. Proceedings of the National Academy of Sciences. 2024 Apr 16;121(16):e2303165121. link
Yang R, Xiao T, Cheng Y, Li A, Qu J, Liang R, Bao S, Wang X, Wang J, Suo J, Luo Q. Sharing massive biomedical data at magnitudes lower bandwidth using implicit neural function. Proceedings of the National Academy of Sciences. 2024 Jul 9;121(28):e2320870121. link
Niu C, Zhang H, Xu C, Hu W, Wu Y, Wu Y, Wang Y, Wu T, Zhu Y, Zhu Y, Wang W. A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption. Proceedings of the National Academy of Sciences. 2024 Dec 17;121(51):e2416294121. link
Zink A, Obermeyer Z, Pierson E. Race adjustments in clinical algorithms can help correct for racial disparities in data quality. Proceedings of the National Academy of Sciences. 2024 Aug 20;121(34):e2402267121. link
Cahuantzi R, Lythgoe KA, Hall I, Pellis L, House T. Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods. Proceedings of the National Academy of Sciences. 2024 Mar 19;121(12):e2317284121. link
Billot B, Magdamo C, Cheng Y, Arnold SE, Das S, Iglesias JE. Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets. Proceedings of the National Academy of Sciences. 2023 Feb 28;120(9):e2216399120. link
He H, Su WJ. A law of data separation in deep learning. Proceedings of the National Academy of Sciences. 2023 Sep 5;120(36):e2221704120. link
Ogawa S, Fumarola F, Mazzucato L. Multitasking via baseline control in recurrent neural networks. Proceedings of the National Academy of Sciences. 2023 Aug 15;120(33):e2304394120. link
Kurvers RH, Nuzzolese AG, Russo A, Barabucci G, Herzog SM, Trianni V. Automating hybrid collective intelligence in open-ended medical diagnostics. Proceedings of the National Academy of Sciences. 2023 Aug 22;120(34):e2221473120. link
Pitti A, Weidmann C, Quoy M. Digital computing through randomness and order in neural networks. Proceedings of the National Academy of Sciences. 2022 Aug 16;119(33):e2115335119. link
Steyvers M, Tejeda H, Kerrigan G, Smyth P. Bayesian modeling of human–AI complementarity. Proceedings of the National Academy of Sciences. 2022 Mar 15;119(11):e2111547119. link
Volzhenin K, Changeux JP, Dumas G. Multilevel development of cognitive abilities in an artificial neural network. Proceedings of the National Academy of Sciences. 2022 Sep 27;119(39):e2201304119. link
Sorscher B, Ganguli S, Sompolinsky H. Neural representational geometry underlies few-shot concept learning. Proceedings of the National Academy of Sciences. 2022 Oct 25;119(43):e2200800119. link
Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, Gerding A, Gneiting T, House KH, Huang Y, Jayawardena D. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences. 2022 Apr 12;119(15):e2113561119. link
Tsao T, Tsao DY. A topological solution to object segmentation and tracking. Proceedings of the National Academy of Sciences. 2022 Oct 11;119(41):e2204248119. link
Saxena R, Shobe JL, McNaughton BL. Learning in deep neural networks and brains with similarity-weighted interleaved learning. Proceedings of the National Academy of Sciences. 2022 Jul 5;119(27):e2115229119. link
Cai S, Li H, Zheng F, Kong F, Dao M, Karniadakis GE, Suresh S. Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease. Proceedings of the National Academy of Sciences. 2021 Mar 30;118(13):e2100697118. link
Rothberg JM, Ralston TS, Rothberg AG, Martin J, Zahorian JS, Alie SA, Sanchez NJ, Chen K, Chen C, Thiele K, Grosjean D. Ultrasound-on-chip platform for medical imaging, analysis, and collective intelligence. Proceedings of the National Academy of Sciences. 2021 Jul 6;118(27):e2019339118. link
Amey JL, Keeley J, Choudhury T, Kuprov I. Neural network interpretation using descrambler groups. Proceedings of the National Academy of Sciences. 2021 Feb 2;118(5):e2016917118. link
Elul Y, Rosenberg AA, Schuster A, Bronstein AM, Yaniv Y. Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis. Proceedings of the National Academy of Sciences. 2021 Jun 15;118(24):e2020620118. link
Bury TM, Sujith RI, Pavithran I, Scheffer M, Lenton TM, Anand M, Bauch CT. Deep learning for early warning signals of tipping points. Proceedings of the National Academy of Sciences. 2021 Sep 28;118(39):e2106140118. link
Wilterson AI, Graziano MS. The attention schema theory in a neural network agent: Controlling visuospatial attention using a descriptive model of attention. Proceedings of the National Academy of Sciences. 2021 Aug 17;118(33):e2102421118. link
Peterfreund E, Lindenbaum O, Dietrich F, Bertalan T, Gavish M, Kevrekidis IG, Coifman RR. Local conformal autoencoder for standardized data coordinates. Proceedings of the National Academy of Sciences. 2020 Dec 8;117(49):30918-27. link
Baldassi C, Pittorino F, Zecchina R. Shaping the learning landscape in neural networks around wide flat minima. Proceedings of the National Academy of Sciences. 2020 Jan 7;117(1):161-70. link
Dezfouli A, Nock R, Dayan P. Adversarial vulnerabilities of human decision-making. Proceedings of the National Academy of Sciences. 2020 Nov 17;117(46):29221-8. link
Doan M, Sebastian JA, Caicedo JC, Siegert S, Roch A, Turner TR, Mykhailova O, Pinto RN, McQuin C, Goodman A, Parsons MJ. Objective assessment of stored blood quality by deep learning. Proceedings of the National Academy of Sciences. 2020 Sep 1;117(35):21381-90. link
Avarguès-Weber A, Finke V, Nagy M, Szabó T, d’Amaro D, Dyer AG, Fiser J. Different mechanisms underlie implicit visual statistical learning in honey bees and humans. Proceedings of the National Academy of Sciences. 2020 Oct 13;117(41):25923-34. link
Kandel ME, Rubessa M, He YR, Schreiber S, Meyers S, Matter Naves L, Sermersheim MK, Sell GS, Szewczyk MJ, Sobh N, Wheeler MB. Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure. Proceedings of the National Academy of Sciences. 2020 Aug 4;117(31):18302-9. link
Sejnowski TJ. The unreasonable effectiveness of deep learning in artificial intelligence. Proceedings of the National Academy of Sciences. 2020 Dec 1;117(48):30033-8. link
Antun V, Renna F, Poon C, Adcock B, Hansen AC. On instabilities of deep learning in image reconstruction and the potential costs of AI. Proceedings of the National Academy of Sciences. 2020 Dec 1;117(48):30088-95. link
Lampinen AK, McClelland JL. Transforming task representations to perform novel tasks. Proceedings of the National Academy of Sciences. 2020 Dec 29;117(52):32970-81. link
Kunda M. AI, visual imagery, and a case study on the challenges posed by human intelligence tests. Proceedings of the National Academy of Sciences. 2020 Nov 24;117(47):29390-7. link
Gennatas ED, Friedman JH, Ungar LH, Pirracchio R, Eaton E, Reichmann LG, Interian Y, Luna JM, Simone CB, Auerbach A, Delgado E. Expert-augmented machine learning. Proceedings of the National Academy of Sciences. 2020 Mar 3;117(9):4571-7. link
Bau D, Zhu JY, Strobelt H, Lapedriza A, Zhou B, Torralba A. Understanding the role of individual units in a deep neural network. Proceedings of the National Academy of Sciences. 2020 Dec 1;117(48):30071-8. link
Fortino V, Wisgrill L, Werner P, Suomela S, Linder N, Jalonen E, Suomalainen A, Marwah V, Kero M, Pesonen M, Lundin J. Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis. Proceedings of the National Academy of Sciences. 2020 Dec 29;117(52):33474-85. link
Krotov D, Hopfield JJ. Unsupervised learning by competing hidden units. Proceedings of the National Academy of Sciences. 2019 Apr 16;116(16):7723-31. link
Belkin M, Hsu D, Ma S, Mandal S. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences. 2019 Aug 6;116(32):15849-54. link
Heer J. Agency plus automation: Designing artificial intelligence into interactive systems. Proceedings of the National Academy of Sciences. 2019 Feb 5;116(6):1844-50. link
Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, Brat DJ, Cooper LA. Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences. 2018 Mar 27;115(13):E2970-9. link
Masse NY, Grant GD, Freedman DJ. Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization. Proceedings of the National Academy of Sciences. 2018 Oct 30;115(44):E10467-75. link
Tang H, Schrimpf M, Lotter W, Moerman C, Paredes A, Ortega Caro J, Hardesty W, Cox D, Kreiman G. Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences. 2018 Aug 28;115(35):8835-40. link
Sokolov I, Dokukin ME, Kalaparthi V, Miljkovic M, Wang A, Seigne JD, Grivas P, Demidenko E. Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: Detection of bladder cancer. Proceedings of the National Academy of Sciences. 2018 Dec 18;115(51):12920-5. link
Ghezzi P, Davies K, Delaney A, Floridi L. Theory of signs and statistical approach to big data in assessing the relevance of clinical biomarkers of inflammation and oxidative stress. Proceedings of the National Academy of Sciences. 2018 Mar 6;115(10):2473-7. link
Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S, Hanel D, Gardner M, Gupta A, Hotchkiss R, Potter H. Deep neural network improves fracture detection by clinicians. Proceedings of the National Academy of Sciences. 2018 Nov 6;115(45):11591-6. link
Flesch T, Balaguer J, Dekker R, Nili H, Summerfield C. Comparing continual task learning in minds and machines. Proceedings of the National Academy of Sciences. 2018 Oct 30;115(44):E10313-22. link
Ma H, Leng S, Aihara K, Lin W, Chen L. Randomly distributed embedding making short-term high-dimensional data predictable. Proceedings of the National Academy of Sciences. 2018 Oct 23;115(43):E9994-10002. link
Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Milan K, Quan J, Ramalho T, Grabska-Barwinska A, Hassabis D. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences. 2017 Mar 28;114(13):3521-6. link
Way SF, Morgan AC, Clauset A, Larremore DB. The misleading narrative of the canonical faculty productivity trajectory. Proceedings of the National Academy of Sciences. 2017 Oct 31;114(44):E9216-23. link
Suzuki S, Jensen EL, Bossaerts P, O’Doherty JP. Behavioral contagion during learning about another agent’s risk-preferences acts on the neural representation of decision-risk. Proceedings of the National Academy of Sciences. 2016 Apr 5;113(14):3755-60. link
Zhang X, Mormino EC, Sun N, Sperling RA, Sabuncu MR, Yeo BT, Alzheimer’s Disease Neuroimaging Initiative. Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease. Proceedings of the National Academy of Sciences. 2016 Oct 18;113(42):E6535-44. link
Honkamaa J, Khan U, Koivukoski S, Valkonen M, Latonen L, Ruusuvuori P, Marttinen P. Deformation equivariant cross-modality image synthesis with paired non-aligned training data. Medical Image Analysis. 2023 Dec 1;90:102940. link
Lin L, Peng L, He H, Cheng P, Wu J, Wong KK, Tang X. Yolocurvseg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation. Medical Image Analysis. 2023 Dec 1;90:102937. link
Ostmeier S, Axelrod B, Isensee F, Bertels J, Mlynash M, Christensen S, Lansberg MG, Albers GW, Sheth R, Verhaaren BF, Mahammedi A. USE-Evaluator: Performance metrics for medical image segmentation models supervised by uncertain, small or empty reference annotations in neuroimaging. Medical Image Analysis. 2023 Dec 1;90:102927. link
Li X, Liang X, Luo G, Wang W, Wang K, Li S. Ambiguity-aware breast tumor cellularity estimation via self-ensemble label distribution learning. Medical Image Analysis. 2023 Dec 1;90:102944. link
Bobrow TL, Golhar M, Vijayan R, Akshintala VS, Garcia JR, Durr NJ. Colonoscopy 3D video dataset with paired depth from 2D-3D registration. Medical image analysis. 2023 Dec 1;90:102956. link
Zhang M, Wu Y, Zhang H, Qin Y, Zheng H, Tang W, Arnold C, Pei C, Yu P, Nan Y, Yang G. Multi-site, multi-domain airway tree modeling. Medical image analysis. 2023 Dec 1;90:102957. link
Yu X, Yang Q, Zhou Y, Cai LY, Gao R, Lee HH, Li T, Bao S, Xu Z, Lasko TA, Abramson RG. Unest: local spatial representation learning with hierarchical transformer for efficient medical segmentation. Medical Image Analysis. 2023 Dec 1;90:102939. link
Xu X, Jia Q, Yuan H, Qiu H, Dong Y, Xie W, Yao Z, Zhang J, Nie Z, Li X, Shi Y. A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images. Medical Image Analysis. 2023 Dec 1;90:102953. link
Andrearczyk V, Oreiller V, Boughdad S, Le Rest CC, Tankyevych O, Elhalawani H, Jreige M, Prior JO, Vallières M, Visvikis D, Hatt M. Automatic head and neck tumor segmentation and outcome prediction relying on FDG-PET/CT images: findings from the second edition of the HECKTOR challenge. Medical image analysis. 2023 Dec 1;90:102972. link
Chen Y, Guo X, Pan Y, Xia Y, Yuan Y. Dynamic feature splicing for few-shot rare disease diagnosis. Medical Image Analysis. 2023 Dec 1;90:102959. link
Fan Z, Gong P, Tang S, Lee CU, Zhang X, Song P, Chen S, Li H. Joint localization and classification of breast masses on ultrasound images using an auxiliary attention-based framework. Medical image analysis. 2023 Dec 1;90:102960. link
Gaillochet M, Desrosiers C, Lombaert H. Active learning for medical image segmentation with stochastic batches. Medical Image Analysis. 2023 Dec 1;90:102958. link
Kascenas A, Sanchez P, Schrempf P, Wang C, Clackett W, Mikhael SS, Voisey JP, Goatman K, Weir A, Pugeault N, Tsaftaris SA. The role of noise in denoising models for anomaly detection in medical images. Medical image analysis. 2023 Dec 1;90:102963. link
Wang AQ, Evan MY, Dalca AV, Sabuncu MR. A robust and interpretable deep learning framework for multi-modal registration via keypoints. Medical image analysis. 2023 Dec 1;90:102962. link
Gu Y, Otake Y, Uemura K, Soufi M, Takao M, Talbot H, Okada S, Sugano N, Sato Y. Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography. Medical Image Analysis. 2023 Dec 1;90:102970. link
Zhao H, Zheng Q, Teng C, Yasrab R, Drukker L, Papageorghiou AT, Noble JA. Memory-based unsupervised video clinical quality assessment with multi-modality data in fetal ultrasound. Medical Image Analysis. 2023 Dec 1;90:102977. link
de Vries L, van Herten RL, Hoving JW, Išgum I, Emmer BJ, Majoie CB, Marquering HA, Gavves E. Spatio-temporal physics-informed learning: A novel approach to CT perfusion analysis in acute ischemic stroke. Medical image analysis. 2023 Dec 1;90:102971. link
Graham MS, Tudosiu PD, Wright P, Pinaya WH, Teikari P, Patel A, Jean-Marie U, Mah YH, Teo JT, Jäger HR, Werring D. Latent Transformer Models for out-of-distribution detection. Medical Image Analysis. 2023 Dec 1;90:102967. link
Pizarro R, Assemlal HE, Jegathambal SK, Jubault T, Antel S, Arnold D, Shmuel A. Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images. Medical Image Analysis. 2023 Dec 1;90:102942. link
Park H, Li B, Liu Y, Nelson MS, Wilson HM, Sifakis E, Eliceiri KW. Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation. Medical image analysis. 2023 Dec 1;90:102961. link
Beetz M, Banerjee A, Ossenberg-Engels J, Grau V. Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images. Medical Image Analysis. 2023 Dec 1;90:102975. link
Xu C, Song Y, Zhang D, Bittencourt LK, Tirumani SH, Li S. Spatiotemporal knowledge teacher–student reinforcement learning to detect liver tumors without contrast agents. Medical Image Analysis. 2023 Dec 1;90:102980. link
La Barbera G, Rouet L, Boussaid H, Lubet A, Kassir R, Sarnacki S, Gori P, Bloch I. Tubular structures segmentation of pediatric abdominal-visceral ceCT images with renal tumors: assessment, comparison and improvement. Medical Image Analysis. 2023 Dec 1;90:102986. link
Zhou B, Xie H, Liu Q, Chen X, Guo X, Feng Z, Hou J, Zhou SK, Li B, Rominger A, Shi K. FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising. Medical image analysis. 2023 Dec 1;90:102993. link
Tian Y, Liu F, Pang G, Chen Y, Liu Y, Verjans JW, Singh R, Carneiro G. Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical image analysis. 2023 Dec 1;90:102930. link
He W, Zhang C, Dai J, Liu L, Wang T, Liu X, Jiang Y, Li N, Xiong J, Wang L, Xie Y. A statistical deformation model-based data augmentation method for volumetric medical image segmentation. Medical image analysis. 2024 Jan 1;91:102984. link
Shahin AH, Zhao A, Whitehead AC, Alexander DC, Jacob J, Barber D. CenTime: Event-conditional modelling of censoring in survival analysis. Medical Image Analysis. 2024 Jan 1;91:103016. link
Liu B, Dolz J, Galdran A, Kobbi R, Ayed IB. Do we really need dice? the hidden region-size biases of segmentation losses. Medical Image Analysis. 2024 Jan 1;91:103015. link
Adiga S, Dolz J, Lombaert H. Anatomically-aware uncertainty for semi-supervised image segmentation. Medical Image Analysis. 2024 Jan 1;91:103011. link
Islam NU, Zhou Z, Gehlot S, Gotway MB, Liang J. Seeking an optimal approach for Computer-aided Diagnosis of Pulmonary Embolism. Medical image analysis. 2024 Jan 1;91:102988. link
Wang Y, Luo Y, Zu C, Zhan B, Jiao Z, Wu X, Zhou J, Shen D, Zhou L. 3D multi-modality Transformer-GAN for high-quality PET reconstruction. Medical Image Analysis. 2024 Jan 1;91:102983. link
Liu P, Ji L, Ye F, Fu B. Advmil: Adversarial multiple instance learning for the survival analysis on whole-slide images. Medical Image Analysis. 2024 Jan 1;91:103020. link
Park S, Lee ES, Shin KS, Lee JE, Ye JC. Self-supervised multi-modal training from uncurated images and reports enables monitoring AI in radiology. Medical Image Analysis. 2024 Jan 1;91:103021. link
Xie Y, Zhang J, Liu L, Wang H, Ye Y, Verjans J, Xia Y. ReFs: A hybrid pre-training paradigm for 3D medical image segmentation. Medical Image Analysis. 2024 Jan 1;91:103023. link
Chen Z, Du Y, Hu J, Liu Y, Li G, Wan X, Chang TH. Mapping medical image-text to a joint space via masked modeling. Medical Image Analysis. 2024 Jan 1;91:103018. link
Fischer M, Bartler A, Yang B. Prompt tuning for parameter-efficient medical image segmentation. Medical Image Analysis. 2024 Jan 1;91:103024. link
Chen Z, Du Y, Hu J, Liu Y, Li G, Wan X, Chang TH. Mapping medical image-text to a joint space via masked modeling. Medical Image Analysis. 2024 Jan 1;91:103018. link
Fischer M, Bartler A, Yang B. Prompt tuning for parameter-efficient medical image segmentation. Medical Image Analysis. 2024 Jan 1;91:103024. link
Kim B, Oh Y, Wood BJ, Summers RM, Ye JC. C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation. Medical Image Analysis. 2024 Jan 1;91:103022. link
Chen B, Zhang Z, Xia D, Sidky EY, Pan X. Prototyping optimization-based image reconstructions from limited-angular-range data in dual-energy CT. Medical Image Analysis. 2024 Jan 1;91:103025. link
Tiwary P, Bhattacharyya K, Prathosh AP. Cycle consistent twin energy-based models for image-to-image translation. Medical Image Analysis. 2024 Jan 1;91:103031. link
Sudre CH, Van Wijnen K, Dubost F, Adams H, Atkinson D, Barkhof F, Birhanu MA, Bron EE, Camarasa R, Chaturvedi N, Chen Y. Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021. Medical Image Analysis. 2024 Jan 1;91:103029. link
Li W, Zhang Y, Zhou H, Yang W, Xie Z, He Y. CLMS: Bridging domain gaps in medical imaging segmentation with source-free continual learning for robust knowledge transfer and adaptation. Medical Image Analysis. 2025 Feb 1;100:103404. link
Zhong S, Wang W, Feng Q, Zhang Y, Ning Z. Cross-view discrepancy-dependency network for volumetric medical image segmentation. Medical Image Analysis. 2025 Jan 1;99:103329. link
Khaledian N, Villard PF, Hammer PE, Perrin DP, Berger MO. Image-based simulation of mitral valve dynamic closure including anisotropy. Medical Image Analysis. 2025 Jan 1;99:103323. link
Manigrasso F, Milazzo R, Russo AS, Lamberti F, Strand F, Pagnani A, Morra L. Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures. Medical Image Analysis. 2025 Jan 1;99:103320. link
Jiang X, Zhang D, Li X, Liu K, Cheng KT, Yang X. Labeled-to-unlabeled distribution alignment for partially-supervised multi-organ medical image segmentation. Medical Image Analysis. 2025 Jan 1;99:103333. link
Huang J, Yang L, Wang F, Wu Y, Nan Y, Wu W, Wang C, Shi K, Aviles-Rivero AI, Schönlieb CB, Zhang D. Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba. Medical Image Analysis. 2025 Jan 1;99:103334. link
Płotka S, Szczepański T, Szenejko P, Korzeniowski P, Calvo JR, Khalil A, Shamshirsaz A, Brawura-Biskupski-Samaha R, Išgum I, Sánchez CI, Sitek A. Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome. Medical Image Analysis. 2025 Jan 1;99:103330. link
Wang CW, Firdi NP, Chu TC, Faiz MF, Iqbal MZ, Li Y, Yang B, Mallya M, Bashashati A, Li F, Wang H. ATEC23 Challenge: Automated prediction of treatment effectiveness in ovarian cancer using histopathological images. Medical Image Analysis. 2025 Jan 1;99:103342. link
Lee W, Wagner F, Galdran A, Shi Y, Xia W, Wang G, Mou X, Ahamed MA, Imran AA, Oh JE, Kim K. Low-dose computed tomography perceptual image quality assessment. Medical Image Analysis. 2025 Jan 1;99:103343. link
Chen S, Garcia-Uceda A, Su J, van Tulder G, Wolff L, van Walsum T, de Bruijne M. Label refinement network from synthetic error augmentation for medical image segmentation. Medical Image Analysis. 2025 Jan 1;99:103355. link
Gu Y, Sun Z, Chen T, Xiao X, Liu Y, Xu Y, Najman L. Dual structure-aware image filterings for semi-supervised medical image segmentation. Medical Image Analysis. 2025 Jan 1;99:103364. link
Zhao C, Esposito M, Xu Z, Zhou W. HAGMN-UQ: Hyper association graph matching network with uncertainty quantification for coronary artery semantic labeling. Medical image analysis. 2025 Jan 1;99:103374. link
Xie K, Yang J, Wei D, Weng Z, Fua P. Efficient anatomical labeling of pulmonary tree structures via deep point-graph representation-based implicit fields. Medical image analysis. 2025 Jan 1;99:103367. link
Lei W, Xu W, Li K, Zhang X, Zhang S. MedLSAM: Localize and segment anything model for 3D CT images. Medical Image Analysis. 2025 Jan 1;99:103370. link
Wang F, Zou Z, Sakla N, Partyka L, Rawal N, Singh G, Zhao W, Ling H, Huang C, Prasanna P, Chen C. TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs. Medical Image Analysis. 2025 Jan 1;99:103373. link
Banduc T, Azzolin L, Manninger M, Scherr D, Plank G, Pezzuto S, Costabal FS. Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature. Medical Image Analysis. 2025 Jan 1;99:103375. link
Lang W, Liu Z, Zhang Y. DACG: Dual Attention and Context Guidance model for radiology report generation. Medical Image Analysis. 2025 Jan 1;99:103377. link
Ma Q, Kaladji A, Shu H, Yang G, Lucas A, Haigron P. Beyond strong labels: Weakly-supervised learning based on Gaussian pseudo labels for the segmentation of ellipse-like vascular structures in non-contrast CTs. Medical Image Analysis. 2025 Jan 1;99:103378. link
Mao Y, Feng Q, Zhang Y, Ning Z. Semantics and instance interactive learning for labeling and segmentation of vertebrae in CT images. Medical Image Analysis. 2025 Jan 1;99:103380. link
Mei L, Deng K, Cui Z, Fang Y, Li Y, Lai H, Tonetti MS, Shen D. Clinical knowledge-guided hybrid classification network for automatic periodontal disease diagnosis in X-ray image. Medical Image Analysis. 2025 Jan 1;99:103376. link
Zhang Z, Keles E, Durak G, Taktak Y, Susladkar O, Gorade V, Jha D, Ormeci AC, Medetalibeyoglu A, Yao L, Wang B. Large-scale multi-center CT and MRI segmentation of pancreas with deep learning. Medical image analysis. 2025 Jan 1;99:103382. link
Lanfredi RB, Mukherjee P, Summers RM. Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification. Medical Image Analysis. 2025 Jan 1;99:103383. link
Tang S, Yan S, Qi X, Gao J, Ye M, Zhang J, Zhu X. Few-shot medical image segmentation with high-fidelity prototypes. Medical Image Analysis. 2025 Feb 1;100:103412. link
Wang KN, Wang H, Zhou GQ, Wang Y, Yang L, Chen Y, Li S. TSdetector: Temporal–Spatial self-correction collaborative learning for colonoscopy video detection. Medical Image Analysis. 2025 Feb 1;100:103384. link
Xie H, Guo L, Velo A, Liu Z, Liu Q, Guo X, Zhou B, Chen X, Tsai YJ, Miao T, Xia M. Noise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision. Medical Image Analysis. 2025 Feb 1;100:103391. link
Camps J, Wang ZJ, Doste R, Berg LA, Holmes M, Lawson B, Tomek J, Burrage K, Bueno-Orovio A, Rodriguez B. Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing. Medical Image Analysis. 2025 Feb 1;100:103361. link
Zhong L, Xiao R, Shu H, Zheng K, Li X, Wu Y, Ma J, Feng Q, Yang W. NCCT-to-CECT synthesis with contrast-enhanced knowledge and anatomical perception for multi-organ segmentation in non-contrast CT images. Medical Image Analysis. 2025 Feb 1;100:103397. link
Wang W, Xia Q, Yan Z, Hu Z, Chen Y, Zheng W, Wang X, Nie S, Metaxas D, Zhang S. AVDNet: joint coronary artery and vein segmentation with topological consistency. Medical Image Analysis. 2024 Jan 1;91:102999. link
Zhang S, Metaxas D. On the challenges and perspectives of foundation models for medical image analysis. Medical image analysis. 2024 Jan 1;91:102996. link
Dai J, Liu T, Torigian DA, Tong Y, Han S, Nie P, Zhang J, Li R, Xie F, Udupa JK. GA-Net: A geographical attention neural network for the segmentation of body torso tissue composition. Medical image analysis. 2024 Jan 1;91:102987. link
Peng J, Wang P, Pedersoli M, Desrosiers C. Boundary-aware information maximization for self-supervised medical image segmentation. Medical Image Analysis. 2024 May 1;94:103150. link
Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D. Self-supervised learning for medical image analysis using image context restoration. Medical image analysis. 2019 Dec 1;58:101539. link
Freitas J, Gomes-Fonseca J, Tonelli AC, Correia-Pinto J, Fonseca JC, Queirós S. Automatic multi-view pose estimation in focused cardiac ultrasound. Medical Image Analysis. 2024 May 1;94:103146. link
Lyu J, Wang S, Tian Y, Zou J, Dong S, Wang C, Aviles-Rivero AI, Qin J. STADNet: Spatial-temporal attention-guided dual-path network for cardiac cine MRI super-resolution. Medical Image Analysis. 2024 May 1;94:103142. link
Gut D, Trombini M, Kucybała I, Krupa K, Rozynek M, Dellepiane S, Tabor Z, Wojciechowski W. Use of superpixels for improvement of inter-rater and intra-rater reliability during annotation of medical images. Medical Image Analysis. 2024 May 1;94:103141. link
Camps J, Berg LA, Wang ZJ, Sebastian R, Riebel LL, Doste R, Zhou X, Sachetto R, Coleman J, Lawson B, Grau V. Digital twinning of the human ventricular activation sequence to clinical 12-lead ECGs and magnetic resonance imaging using realistic Purkinje networks for in silico clinical trials. Medical Image Analysis. 2024 May 1;94:103108. link
Schmidt A, Mohareri O, DiMaio S, Yip MC, Salcudean SE. Tracking and mapping in medical computer vision: A review. Medical Image Analysis. 2024 May 1;94:103131. link
Zhu Z, Ma X, Wang W, Dong S, Wang K, Wu L, Luo G, Wang G, Li S. Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation. Medical image analysis. 2024 May 1;94:103112. link
Berenguer AD, Kvasnytsia M, Bossa MN, Mukherjee T, Deligiannis N, Sahli H. Semi-supervised medical image classification via distance correlation minimization and graph attention regularization. Medical image analysis. 2024 May 1;94:103107. link
Su J, Luo Z, Lian S, Lin D, Li S. Mutual learning with reliable pseudo label for semi-supervised medical image segmentation. Medical Image Analysis. 2024 May 1;94:103111. link
Chen W, Zhao W, Chen Z, Liu T, Liu L, Liu J, Yuan Y. Mask-aware transformer with structure invariant loss for CT translation. Medical Image Analysis. 2024 Aug 1;96:103205. link
Mo Y, Liu F, Yang G, Wang S, Zheng J, Wu F, Papież BW, McIlwraith D, He T, Guo Y. Labelling with dynamics: A data-efficient learning paradigm for medical image segmentation. Medical Image Analysis. 2024 Jul 1;95:103196. link
Schmidt K, Bearce B, Chang K, Coombs L, Farahani K, Elbatel M, Mouheb K, Marti R, Zhang R, Zhang Y, Wang Y. Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge. Medical Image Analysis. 2024 Jul 1;95:103206. link
Zhu W, Jin Y, Ma G, Chen G, Egger J, Zhang S, Metaxas DN. Classification of lung cancer subtypes on CT images with synthetic pathological priors. Medical Image Analysis. 2024 Jul 1;95:103199. link
Martín-Saladich Q, Pericàs JM, Ciudin A, Ramirez-Serra C, Escobar M, Rivera-Esteban J, Aguadé-Bruix S, Ballester MA, Herance JR. Metabolic-associated fatty liver voxel-based quantification on CT images using a contrast adapted automatic tool. Medical Image Analysis. 2024 Jul 1;95:103185. link
Meng Y, Zhang Y, Xie J, Duan J, Joddrell M, Madhusudhan S, Peto T, Zhao Y, Zheng Y. Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment. Medical Image Analysis. 2024 Jul 1;95:103183. link
Liu Q, Tsai YJ, Gallezot JD, Guo X, Chen MK, Pucar D, Young C, Panin V, Casey M, Miao T, Xie H. Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification. Medical Image Analysis. 2024 Jul 1;95:103180. link
Stan S, Rostami M. Unsupervised model adaptation for source-free segmentation of medical images. Medical Image Analysis. 2024 Jul 1;95:103179. link
Guo X, Shi L, Chen X, Liu Q, Zhou B, Xie H, Liu YH, Palyo R, Miller EJ, Sinusas AJ, Staib L. TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction. Medical Image Analysis. 2024 Aug 1;96:103190. link
Chen Y, Liu Y, Wang C, Elliott M, Kwok CF, Peña-Solorzano C, Tian Y, Liu F, Frazer H, McCarthy DJ, Carneiro G. BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations. Medical image analysis. 2024 Aug 1;96:103192. link
Jiao J, Zhou J, Li X, Xia M, Huang Y, Huang L, Wang N, Zhang X, Zhou S, Wang Y, Guo Y. Usfm: A universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis. Medical image analysis. 2024 Aug 1;96:103202. link
D‘Souza NS, Wang H, Giovannini A, Foncubierta-Rodriguez A, Beck KL, Boyko O, Syeda-Mahmood TF. Fusing modalities by multiplexed graph neural networks for outcome prediction from medical data and beyond. Medical Image Analysis. 2024 Apr 1;93:103064. link
Harnod Z, Lin C, Yang HW, Wang ZW, Huang HL, Lin TY, Huang CY, Lin LY, Young HW, Lo MT. A transferable in-silico augmented ischemic model for virtual myocardial perfusion imaging and myocardial infarction detection. Medical Image Analysis. 2024 Apr 1;93:103087. link
Pankewitz LR, Hustad KG, Govil S, Perry JC, Hegde S, Tang R, Omens JH, Young AA, McCulloch AD, Arevalo HJ. A universal biventricular coordinate system incorporating valve annuli: Validation in congenital heart disease. Medical image analysis. 2024 Apr 1;93:103091. link
Sun J, Wei D, Wang L, Zheng Y. Hybrid unsupervised representation learning and pseudo-label supervised self-distillation for rare disease imaging phenotype classification with dispersion-aware imbalance correction. Medical Image Analysis. 2024 Apr 1;93:103102. link
Chen X, Zhou B, Xie H, Guo X, Zhang J, Duncan JS, Miller EJ, Sinusas AJ, Onofrey JA, Liu C. DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT. Medical image analysis. 2023 Aug 1;88:102840. link
Ferdian E, Marlevi D, Schollenberger J, Aristova M, Edelman ER, Schnell S, Figueroa CA, Nordsletten DA, Young AA. Cerebrovascular super-resolution 4D Flow MRI–Sequential combination of resolution enhancement by deep learning and physics-informed image processing to non-invasively quantify intracranial velocity, flow, and relative pressure. Medical Image Analysis. 2023 Aug 1;88:102831. link
Huttinga NR, Bruijnen T, van den Berg CA, Sbrizzi A. Gaussian Processes for real-time 3D motion and uncertainty estimation during MR-guided radiotherapy. Medical Image Analysis. 2023 Aug 1;88:102843. link
Khor HG, Ning G, Sun Y, Lu X, Zhang X, Liao H. Anatomically constrained and attention-guided deep feature fusion for joint segmentation and deformable medical image registration. Medical Image Analysis. 2023 Aug 1;88:102811. link
Xu K, Li T, Khan MS, Gao R, Antic SL, Huo Y, Sandler KL, Maldonado F, Landman BA. Body composition assessment with limited field-of-view computed tomography: A semantic image extension perspective. Medical image analysis. 2023 Aug 1;88:102852. link
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
Liu X, Prince JL, Xing F, Zhuo J, Reese T, Stone M, El Fakhri G, Woo J. Attentive continuous generative self-training for unsupervised domain adaptive medical image translation. Medical image analysis. 2023 Aug 1;88:102851. link
Li L, Ding W, Huang L, Zhuang X, Grau V. Multi-modality cardiac image computing: A survey. Medical image analysis. 2023 Aug 1;88:102869. link
Messaoudi H, Belaid A, Salem DB, Conze PH. Cross-dimensional transfer learning in medical image segmentation with deep learning. Medical image analysis. 2023 Aug 1;88:102868. link
Xu Z, Wang Y, Lu D, Luo X, Yan J, Zheng Y, Tong RK. Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation. Medical Image Analysis. 2023 Aug 1;88:102880. link
Xu X, Chen Y, Wu J, Lu J, Ye Y, Huang Y, Dou X, Li K, Wang G, Zhang S, Gong W. A novel one-to-multiple unsupervised domain adaptation framework for abdominal organ segmentation. Medical Image Analysis. 2023 Aug 1;88:102873. link
Xing X, Chen Z, Hou Y, Yuan Y. Gradient modulated contrastive distillation of low-rank multi-modal knowledge for disease diagnosis. Medical image analysis. 2023 Aug 1;88:102874. link
Chaitanya K, Erdil E, Karani N, Konukoglu E. Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation. Medical image analysis. 2023 Jul 1;87:102792. link
Bahadormanesh N, Tomka B, Kadem M, Khodaei S, Keshavarz-Motamed Z. An ultrasound-exclusive non-invasive computational diagnostic framework for personalized cardiology of aortic valve stenosis. Medical Image Analysis. 2023 Jul 1;87:102795. link
Aggarwal A, Mortensen P, Hao J, Kaczmarczyk Ł, Cheung AT, Al Ghofaily L, Gorman RC, Desai ND, Bavaria JE, Pouch AM. Strain estimation in aortic roots from 4D echocardiographic images using medial modeling and deformable registration. Medical image analysis. 2023 Jul 1;87:102804. link
Li L, Wu F, Wang S, Luo X, Martín-Isla C, Zhai S, Zhang J, Liu Y, Zhang Z, Ankenbrand MJ, Jiang H. MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images. Medical Image Analysis. 2023 Jul 1;87:102808. link
Murugesan B, Liu B, Galdran A, Ayed IB, Dolz J. Calibrating segmentation networks with margin-based label smoothing. Medical Image Analysis. 2023 Jul 1;87:102826. link
Xia Y, Ravikumar N, Lassila T, Frangi AF. Virtual high-resolution MR angiography from non-angiographic multi-contrast MRIs: synthetic vascular model populations for in-silico trials. Medical Image Analysis. 2023 Jul 1;87:102814. link
Gao H, Lyu M, Zhao X, Yang F, Bai X. Contour-aware network with class-wise convolutions for 3D abdominal multi-organ segmentation. Medical Image Analysis. 2023 Jul 1;87:102838. link
Li W, Zhang Y, Zhou H, Yang W, Xie Z, He Y. CLMS: Bridging domain gaps in medical imaging segmentation with source-free continual learning for robust knowledge transfer and adaptation. Medical Image Analysis. 2025 Feb 1;100:103404. link
Zhong S, Wang W, Feng Q, Zhang Y, Ning Z. Cross-view discrepancy-dependency network for volumetric medical image segmentation. Medical Image Analysis. 2025 Jan 1;99:103329. link
Khaledian N, Villard PF, Hammer PE, Perrin DP, Berger MO. Image-based simulation of mitral valve dynamic closure including anisotropy. Medical Image Analysis. 2025 Jan 1;99:103323. link
Manigrasso F, Milazzo R, Russo AS, Lamberti F, Strand F, Pagnani A, Morra L. Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures. Medical Image Analysis. 2025 Jan 1;99:103320. link
Jiang X, Zhang D, Li X, Liu K, Cheng KT, Yang X. Labeled-to-unlabeled distribution alignment for partially-supervised multi-organ medical image segmentation. Medical Image Analysis. 2025 Jan 1;99:103333. link
Huang J, Yang L, Wang F, Wu Y, Nan Y, Wu W, Wang C, Shi K, Aviles-Rivero AI, Schoenlieb CB, Zhang D. Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba. Medical Image Analysis. 2025 Jan 1;99:103334. link
Lee W, Wagner F, Galdran A, Shi Y, Xia W, Wang G, Mou X, Ahamed MA, Imran AA, Oh JE, Kim K. Low-dose computed tomography perceptual image quality assessment. Medical Image Analysis. 2025 Jan 1;99:103343. link
Chen S, Garcia-Uceda A, Su J, van Tulder G, Wolff L, van Walsum T, de Bruijne M. Label refinement network from synthetic error augmentation for medical image segmentation. Medical Image Analysis. 2025 Jan 1;99:103355. link
Gu Y, Sun Z, Chen T, Xiao X, Liu Y, Xu Y, Najman L. Dual structure-aware image filterings for semi-supervised medical image segmentation. Medical Image Analysis. 2025 Jan 1;99:103364. link
Zhao C, Esposito M, Xu Z, Zhou W. HAGMN-UQ: Hyper association graph matching network with uncertainty quantification for coronary artery semantic labeling. Medical image analysis. 2025 Jan 1;99:103374. link
Xie K, Yang J, Wei D, Weng Z, Fua P. Efficient anatomical labeling of pulmonary tree structures via deep point-graph representation-based implicit fields. Medical image analysis. 2025 Jan 1;99:103367. link
Lei W, Xu W, Li K, Zhang X, Zhang S. MedLSAM: Localize and segment anything model for 3D CT images. Medical Image Analysis. 2025 Jan 1;99:103370. link
Banduc T, Azzolin L, Manninger M, Scherr D, Plank G, Pezzuto S, Costabal FS. Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature. Medical Image Analysis. 2025 Jan 1;99:103375. link
Ma Q, Kaladji A, Shu H, Yang G, Lucas A, Haigron P. Beyond strong labels: Weakly-supervised learning based on Gaussian pseudo labels for the segmentation of ellipse-like vascular structures in non-contrast CTs. Medical Image Analysis. 2025 Jan 1;99:103378. link
Mao Y, Feng Q, Zhang Y, Ning Z. Semantics and instance interactive learning for labeling and segmentation of vertebrae in CT images. Medical Image Analysis. 2025 Jan 1;99:103380. link
Zhang Z, Keles E, Durak G, Taktak Y, Susladkar O, Gorade V, Jha D, Ormeci AC, Medetalibeyoglu A, Yao L, Wang B. Large-scale multi-center CT and MRI segmentation of pancreas with deep learning. Medical image analysis. 2025 Jan 1;99:103382. link
Xie H, Guo L, Velo A, Liu Z, Liu Q, Guo X, Zhou B, Chen X, Tsai YJ, Miao T, Xia M. Noise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision. Medical Image Analysis. 2025 Feb 1;100:103391. link
Camps J, Wang ZJ, Doste R, Berg LA, Holmes M, Lawson B, Tomek J, Burrage K, Bueno-Orovio A, Rodriguez B. Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing. Medical Image Analysis. 2025 Feb 1;100:103361. link
Zhong L, Xiao R, Shu H, Zheng K, Li X, Wu Y, Ma J, Feng Q, Yang W. NCCT-to-CECT synthesis with contrast-enhanced knowledge and anatomical perception for multi-organ segmentation in non-contrast CT images. Medical Image Analysis. 2025 Feb 1;100:103397. link
Sun L, Han B, Jiang W, Liu W, Liu B, Tao D, Yu Z, Li C. Multi-scale region selection network in deep features for full-field mammogram classification. Medical Image Analysis. 2025 Feb 1;100:103399. link
Li W, Zhang Y, Zhou H, Yang W, Xie Z, He Y. CLMS: Bridging domain gaps in medical imaging segmentation with source-free continual learning for robust knowledge transfer and adaptation. Medical Image Analysis. 2025 Feb 1;100:103404. link