Publications

Conference

2025

1. T. Haroon, MG Kibria, M.B. Hossain, "Morphology-Inspired Optimization with Deep Learning for Sedimentary Particle Segmentation in Petrographic Microscope Images," in Proc. 2025 IEEE International Conference Machine Learning and Applications (ICMLA 2025), Florida, USA, Dec. 2–6, 2025 (Accepted)

2. T.A.B. Haroon, M.G. Kibria, A. McKinney, and M.B. Hossain, "From Pixels to Pores: Advancing Formation Characterization through Deep Learning Analysis," in Proc. 2025 Geological Society of America Connects Meeting (CONNECT 2025), Vol. 57, No.6, San Antonio, Texas, USA, Oct. 19-22, 2025

3. G. Jilani and M. B. Hossain, "Generative diffusion-augmented learning for lesion detection in digital breast tomosynthesis: A proof-of-concept study," in Proc. 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2025), Vienna, Austria, Oct. 5–8, 2025

4. R. Ganjee, T. Mustafaev, M. B. Hossain, M. Zuley, and J. Lee, "Developing an Attention-Based breast dense tissue segmentation algorithm for multi-center, multi-vendor data," in Proc. 2025 Radiological Society of North America (RSNA 2025), Chicago, IL, USA (Accepted)

5. R. Ganjee, T. Mustafaev, M. B. Hossain, M. Zuley, J. Lee, "Testing validity of FFDM-based density evaluating AI algorithm for Synthetic Mammograms," in Proc. 11th International Breast Density and Cancer Risk Assessment Workshop (IBDW 2025), Kauaʻi Island, HI, USA, Jun. 4–6, 2025

6. M. B. Hossain, T. Mustafaev, R. Nishikawa, and J. Lee, "Developing breast dense tissue segmentation algorithm in digital breast tomosynthesis," in Proc. 2025 SPIE Medical Imaging: Computer-Aided Diagnosis (MI2025), San Diego, CA, Feb 16-20, 2025, https://doi.org/10.1117/12.3047307

7. T. Mustafaev, M. B. Hossain, R. Nishikawa, and J. Lee, "AI-driven race prediction from mammographic images: anatomical insights for AI model's bias mitigation," in Proc. 2025 SPIE Medical Imaging: Computer-Aided Diagnosis (MI 2025), San Diego, CA, Feb 16-20, 2025, https://doi.org/10.1117/12.3047312

2021- 2024

8. T. Mustafaev, M. B. Hossain, R.M. Nishikawa, and J. Lee, "Exploring anatomical race differences in mammographic images using AI: preliminary results", in Proc. 2024 Radiological Society of North America (RSNA 2024), Chicago, IL, USA, Dec. 1-5, 2024 

9. M. B. Hossain, R.M. Nishikawa, and J. Lee, "Sample-efficient framework for breast lesion detection in Digital Breast Tomosynthesis: preliminary analysis on its generalizability," in Proc. 17th IW. Breast imaging (IWBI 2024), Chicago, IL, Feb 16-20, 2024 https://doi.org/10.1117/12.3047307

10. M. B. Hossain, R.M. Nishikawa, and J. Lee, "Developing an image-domain transformation technique for adapting deep learning algorithms: preliminary work using simulated Tomosynthesis of breast patches," in Proc. 2024 SPIE Medical Imaging: Computer-Aided Diagnosis (MI 2024), San Diego, CA, Feb 16-20, 2024

11. B. Bruno, C. Choi, J. Teixeira, M Dustler, R. Englander, T Rego, Y. Malheiros, T. Filho, M. B. Hossain, J. Lee and A. Maidment, "Representation of complex mammary parenchyma texture in tomosynthesis using simplex noise simulations" in Proc. 2024 SPIE Medical Imaging: Computer-Aided Diagnosis (MI 2024), San Diego, CA, Feb 16-20, 2024

12. J Lee, M. B. Hossain, RM Nishikawa, A Bandos, M Zuley, "Longitudinal Mammographic Breast Percent Density Changes for Breast Cancer Risk Estimation: Proof-of-concept study," in Proc. 11th International Breast Density and Cancer Risk Assessment Workshop (IBDW 2023), HI, USA, Jun. 4–6, 2023

13. M.B. Hossain and J. Lee, “Developing a task-oriented deep convolutional neural network application towards estimating near-term breast cancer risk: Preliminary work,” in Proc. SPIE 12033, Medical Imaging 2023: Computer-Aided Diagnosis (MI 2023), Feb. 2023.

14. M.B. Hossain, R. M. Nishikawa, and J. Lee, “Improving lesion detection algorithm in digital breast tomosynthesis leveraging ensemble cross-validation models with multi-depth levels,” in Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis (MI 2022), Apr. 2022, doi: 10.1117/12.2611007. 

2015 - 2020

15. M.B. Hossain, S. Nishio, H. Takafumio, and S. Kobashi, “A deep learning approach for surgical instruments detection in Orthopaedic surgery videos using transfer learning,” in Proc. SPIE Medical Imaging: Image-Guided Proc., Rob. Interv., & Modeling, Feb. 2020, doi: 10.1117/12.2550670.

16. S. Kobashi, S. Nishio, and M.B. Hossain, “Knee Replacement Surgery Phase Recognition with Wearable Camera,” in Proc. International Workshop on Advanced Image Technology, Bali, Indonesia, 2020. 

17. S. Nishio, M.B. Hossain, M. Nii, T. Yagi, H. Takafumio, and S. Kobashi, “Surgical Phase Recognition Method with a Sequential Consistency for CAOS-AI Navigation System,” in 2020 IEEE Global Conference on Life Sciences and Technologies (LifeTech 2020), Mar. 2020, doi: 10.1109/LifeTech48969.2020.1570619203. 

18. M.B. Hossain, S. Nishio, H. Takafumi, and S. Kobashi, “Computer-aided system for operating room nurses during knee surgery,” in 6th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII 2019), Chengdu, China, Nov. 2019. 

19. M.B. Hossain, T. Morooka, M. Okuno, M. Nii, S. Yoshiya, and S. Kobashi, “A Supervised Machine Learning Based Method For TKA Outcome Prediction,” in Annual meeting of Orthopaedic Research Society (ORS 2019), Austin, TX, Feb. 2019. 

20. M.B. Hossain, T. Morooka, M. Okuno, M. Nii, S. Yoshiya, and S. Kobashi, “Implanted Knee Kinematics Prediction: comparative performance analysis of machine learning techniques,” in 2018 7th International Conference on Informatics, Electronics & Vision (ICIEV 2018), Jun. 2018, doi: 10.1109/ICIEV.2018.8640999. 

21. Y. Kubo, M.B. Hossain, T. Muto, H. Tanaaka, H. Inui, K. Nobuhara, and S. Kobashi, “3-D Statistical Shape Model of the Humerus Towards Artificial Shoulder Joint Design,” in Proc. of the World Automation Congress (WAC 2018), Washington, USA, Jun. 2018. 

22. M.B. Hossain, M. Nii, S. Yoshiya, and S. Kobashi, “Computer-Aided Knee Surgery: Automated Anatomical Axis Definition of Clinical Interest,” in Proc. of 17th International Symposium on Advanced Intelligent Systems (ISIS), Sept. 2017. (Best paper award)

23. M.B. Hossain, M. Nii, T. Morooka, M. Okuno, S. Yoshiya, and S. Kobashi, “Post-operative implanted knee kinematics prediction in total knee arthroscopy using clinical big data,” in Lecture Notes in Computer Science, vol. 9835. Springer, 2016, pp. 405–412, doi: 10.1007/978-3-319-43518-3_39. 

24. M.B. Hossain, M. Nii, and S. Kobashi, “Construction of statistical shape model of femoral bone using MR images,” in Proc. of the 5th Int. Conf. on Informatics, Electronics and Vision (ICIEV 2016), 2016, pp. 658–662. 

25. S. Kobashi, M.B. Hossain, M. Nii, S. Kambara, T. Morooka, M. Okuno, and S. Yoshiya, “Prediction of post-operative implanted knee function using machine learning in clinical big data,” in Proc. of the Int. Conf. on Machine Learning and Cybernetics (ICMLC 2016), 2016, pp. 195–200. 

26. M. Yasugi, M.B. Hossain, M. Nii, M. Morimoto, and S. Kobashi, “Cerebral aneurysm occurrence prediction by morphometric analysis of the Willis ring,” in Proc. of 2016 IEEE Int. Conf. on Systems, Man, Cybernetics (SMC 2016), 2016, pp. 1774–1779. 

27. M. Yasugi , M.B. Hossain, H. Shibuya, T. Nomura, M. Arai, M. Morimoto , S. Kobashi, "Prediction of Cerebral Aneurysm Occurrence Using 3D Shape of Cerebral Arterial Annulus ," 2016 Joint Research Meeting of the Japanese Society for Artificial Intelligence, 2016 (Research paper Award)

28. K. Morita, M.B. Hossain, ... S. Kobashi, “Pseudo Lateral Radiograph Synthesis Method for Computer-aided Surgical Planning of Anterior Cruciate Ligament Reconstruction,” in Proc. of the 7th Int. Conf. on Emerging Trends in Engineering & Technology (ICETET), 2015.

Journal

1. N. Konz, M. Buda, H. Gu, A. Saha, J. Yang, J. Chłędowski, J. Park, J. Witowski, K.J. Geras, Y. Shoshan, F.G. Solomon, D. Khapun, V. Ratner, E. Barkan, M. O. Flato, R. Marti, A. Omigbodun, C. Marasinou, N. Nakhaei, W. Hsu, P. Sahu, M.B. Hossain, J. Lee, C. Santos, A. Przelaskowski, D. Cline, J.K. Cramer, B. Bearce, K. Cha, K. Farahani, N. Petrick, L. Hadjiiski, K. Drukker, S. Armato, M. Mazurowski, "A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis", JAMA Netw. Open, vol. 6, no. 2, p. e230524, 2023, doi:10.1001/jamanetworkopen.2023.0524.

2. M. B. Hossain, R. M. Nishikawa, and J. Lee, “Developing breast lesion detection algorithms for Digital Breast Tomosynthesis: Leveraging false positive findings,” Medical Physics, 2022, doi: 10.1002/mp.15883.

3. S. Nishio, M.B. Hossain, M. Nii, T. Hiranaka, and S. Kobashi, “Surgical Phase Recognition with Wearable Video Camera for Computer-aided Orthopaedic Surgery-AI Navigation System,” International Journal of Affective Engineering (IJAE), vol. 19, no. 2, pp. 137–143, 2020. 

4. M.B. Hossain, T. Morooka, M. Okuno, M. Nii, S. Yoshiya, and S. Kobashi, “Surgical outcome prediction in total knee arthroplasty using machine learning,” J. of Intelligent automation & Soft Computing, vol. 25, no. 1, pp. 105–115, 2019, doi: 10.31209/2018.100000034. 

5. M. Yasugi, M.B. Hossain, M. Nii, and S. Kobashi, "Relationship between cerebral aneurysm development and cerebral artery shape," J. of Advanced Computational Intelligence and Intelligent Informatics, vol. 22, no. 2, pp. 249-255, 2018, doi:10.20965/jaciii.2018.p0249.

6. M.B. Hossain, M. Nii, S. Yoshiya, and S. Kobashi, "Fully-automated femoral coordinate system definition for constructing statistical model of distal femur," Int. J. of Biomedical Soft Computing and Human Sciences, vol. 22, no. 2, pp. 73–83, 2017, doi: 10.24466/ijbschs.22.2_73. 

7. M.E.H. Chowdhury, A. Khandakar, K. Mullinger, M.B. Hossain, N. A. Emadi, A. Antunes, and R. Bowtell, “Reference Layer Artefact Subtraction (RLAS): Electromagnetic Simulations,” IEEE Access, vol. 7, pp. 17882–17895, 2019, doi: 10.1109/ACCESS.2019.2896580. 

8. M.E.H. Chowdhury, A. Khandakar, M.B. Hossain, and K. Alzoubi, “Effects of the Phantom Shape on the Gradient Artefact of Electroencephalography (EEG) Data in Simultaneous EEG–fMRI,” Applied Sciences, vol. 8, no. 10, p. 1969, 2018, doi: 10.3390/app8101969.

Book Chapter

1. M.B. Hossain, and S. Kobashi, "Prediction of Personalized Postoperative Implanted Knee Kinematics with Statistical Temporal Modeling," in Multidisciplinary Computational Anatomy: Springer, Singapore, 2022, pp. 275-281. https://doi.org/10.1007/978-981-16-4325-5_36 

Workshop Talk

1. M.B. Hossain, "Computer-aided knee surgery: fully-automated landmark and coordinate system," IIWSC: International workshop on soft computing - theory and applications in medical engineering, AMEC, University of Hyogo, Himeji, June 26, 2017