Faculty

Professor Saha’s research lies at the intersection of public health, epidemiology, biomedical informatics and data science. His work focuses on understanding the factors that influence disease risk, early detection, health outcomes and survivorship across populations. He is particularly interested in generating evidence that can support disease prevention, improve population health and inform public health policy and practice.

A major focus of his research is cancer and population concerning health challenges that adversely affect the life’s longevity. His work seeks to understand how diverse sources of health information, including clinical records, pathology and radiology data, genomic information and population-based datasets, can be used to better understand disease patterns, identify individuals at higher risk and support strategies for prevention and early detection. Through this research, he aims to contribute to more effective and equitable approaches to disease control and healthcare delivery.

Professor Saha also works on broader methodological and population health questions that extend beyond cancer and life expectancy condition. His previous research has included studies related to infectious diseases, critical care and clinical decision support. At Ahmedabad University, he aims to collaborate with researchers across public health, life sciences, engineering, data science, social sciences, management and clinical disciplines to address health challenges relevant to a new direction focusing the affected or, high/low risk population in India and South Asia.

A cross-cutting theme of his work is the responsible and reproducible use of health data for the public good. He is interested in developing analytical approaches and research tools that strengthen evidence generation, support transparent research practices and enable collaborative efforts to improve health outcomes at the population level.

PEER-REVIEWED JOURNAL ARTICLES

• Saha, M., Tran, T.-V.-T., Bhawsar, P.M.S., Zhang, T., Zhao, W., Hoang, P.H., Mutreja, K., Lawrence, S.M., Rothman, N., Lan, Q., Homer, R., Baine, M.K., Sholl, L.M., Joubert, P., Leduc, C., Travis, W.D., Chanock, S.J., Shi, J., Yang, S.-R., Almeida, J.S., Landi, M.T., 2026. Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning. Modern Pathology, p.100973.
• Saha, M., Abubakar, M., Pfeiffer, R.M., Rohan, T.E., Duggan, M.A., Richert-Boe, K., Almeida, J.S. and Gierach, G.L., 2025. Deep learning analysis of hematoxylin and eosin-stained benign breast biopsies to predict future invasive breast cancer. JNCI Cancer Spectrum, p.pkaf037.
• Diaz-Gay, M., Zhang, T., Hoang, P.H., Leduc, C., Baine, M.K., Travis, W.D., Sholl, L.M., Joubert, P., Khandekar, A., Zhao, W., Steele, C.D., Otlu, B., Nandi, S.P., Vangara, R., Bergstrom, E.N., Kazachkova, M., Pich, O., Swanton, C., Hsiung, C.A., Chang, I.S., Wong, M.P., Leung, K.C., Sang, J., McElderry, J.P., Hartman, C., Colon-Matos, F.J., Miraftab, M., Saha, M., Lee, O.W., Jones, K.M., Gallego-Garcia, P., Yang, Y., Zhong, X., Edell, E.S., Santamaria, J.M., Schabath, M.B., Yendamuri, S.S., Manczuk, M., Lissowska, J., Swiatkowska, B., Mukeria, A., Shangina, O., Zaridze, D., Holcatova, I., Mates, D., Milosavljevic, S., Kontic, M., Bosse, Y., Rothberg, B.E.G., Christiani, D.C., Gaborieau, V., Brennan, P., Liu, G., Hofman, P., Yang, L., Nowak, M.A., Shi, J., Rothman, N., Wedge, D.C., Homer, R., Yang, S.R., Pesatori, A.C., Consonni, D., Lan, Q., Zhu, B., Chanock, S.J., Choi, J., Alexandrov, L.B. and Landi, M.T., 2025. The mutagenic forces shaping the genomes of lung cancer in never smokers. Nature, 644(8075), pp. 133–144.
• Zhang, T., Zhao, W., Wirth, C., Diaz-Gay, M., Yin, J., Cecati, M., Marchegiani, F., Hoang, P.H., Leduc, C., Baine, M.K., Travis, W.D., Sholl, L.M., Joubert, P., Sang, J., McElderry, J.P., Klein, A., Khandekar, A., Hartman, C., Rosenbaum, J., Colon-Matos, F.J., Miraftab, M., Saha, M., Lee, O.W., Jones, K.M., Caporaso, N.E., Wong, M.P., Leung, K.C., Hsiung, C.A., Chen, C.Y., Edell, E.S., Martínez Santamaría, J., Schabath, M.B., Yendamuri, S.S., Manczuk, M., Lissowska, J., Świątkowska, B., Mukeria, A., Shangina, O., Zaridze, D., Holcatova, I., Mates, D., Milosavljevic, S., Savic, M., Bosse, Y., Gould Rothberg, B.E., Christiani, D.C., Gaborieau, V., Brennan, P., Liu, G., Hofman, P., Homer, R., Yang, S.R., Pesatori, A.C., Consonni, D., Yang, L., Zhu, B., Shi, J., Brown, K., Rothman, N., Chanock, S.J., Alexandrov, L.B., Choi, J., Cardelli, M., Lan, Q., Nowak, M.A., Wedge, D.C. and Landi, M.T., 2025. Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition. Nature (Accepted).
• Veiga, L.H.S., Gierach, G.L., Smith, S.A., Howell, R.M., Mille, M.M., Saha, M., Curtis, R.E., Ramin, C., Bodelon, C., Feigelson, H.S., Bowles, E.J.A., Buist, D.S.M., Weinmann, S., Vo, J.B., Lee, C. and Berrington de Gonzalez, A., 2025. Contralateral breast cancer after radiotherapy and hormone therapy in two cohorts of US breast cancer survivors. British Journal of Cancer, pp.1-10.
• Zhao, W., Zhang, T., Hua, X., Hoang, P.H., Miraftab, M., Saha, M., McElderry, J.P., Sang, J., Lee, O.W., Hartman, C., Khandekar, A., Sharma, S., Colon-Matos, F.J., Anyaso-Samuel, S., Wang, D., Jones, K., Hutchinson, A., Hicks, B., Rosenbaum, J., Zhong, X., Yang, Y., Pesatori, A., Consonni, D., Christiani, D.C., Leung, K.C., Wong, M.P., Manczuk, M., Lissowska, J., Świątkowska, B., Mukeria, A., Shangina, O., Zaridze, D., Holcatova, I., Mates, D., Milosavljevic, S., Ognjanovic, S., Savic, M., Kontic, M., Gaborieau, V., Brennan, P., Arrieta, O., Bossé, Y., Edell, E.S., Schabath, M.B., Hofman, P., Mas, L., Yendamuri, S.S., Chen, C.Y., Chang, I.S., Hsiung, C.A., Liu, G., Martínez Santamaría, J., Gould Rothberg, B.E., Mutreja, K., Lawrence, S., Rothman, N., Alexandrov, L.B., Leduc, C., Baine, M.K., Joubert, P., Sholl, L.M., Travis, W.D., Homer, R., Lan, Q., Chanock, S.J., Yang, L., Yang, S.R., Shi, J. and Landi, M.T., 2026. A prognostic signature for lung adenocarcinoma in patients who have never smoked. Cancer Discovery, pp.OF1-OF18.
• McElderry, J.P., Zhang, T., Zhao, W., Hoang, P.H., Anyaso-Samuel, S., Sang, J., Khandekar, A., Hartman, C., Colón-Matos, F.J., Miraftab, M., Saha, M., Lee, O.W., Sharma, S., Jones, K.M., Zhu, B., Díaz-Gay, M., Mas, L., Rodriguez, O., Edell, E.S., Santamaría, J., Schabath, M.B., Yendamuri, S.S., Manczuk, M., Lissowska, J., Świątkowska, B., Mukeria, A., Shangina, O., Zaridze, D., Holcatova, I., Janout, V., Mates, D., Ognjanovic, S., Savic, M., Kontic, M., Bossé, Y., Gould Rothberg, B.E., Christiani, D.C., Gaborieau, V., Brennan, P., Liu, G., Hofman, P., Wong, M.P., Leung, K.C., Chen, C.Y., Hsiung, C.A., Rothman, N., Leduc, C., Baine, M., Travis, W.D., Sholl, L.M., Joubert, P., Homer, R., Yang, S.R., Lan, Q., Nowak, M.A., Wedge, D.C., Alexandrov, L.B., Chanock, S.J., Vogtmann, E., Abnet, C., Shi, J., and Landi, M.T., 2026. Microbiome analysis of 940 lung cancers in never-smokers reveals lack of clinically relevant associations. Nature Communications, 17, 192.
• Afonso, M., Bhawsar, P.M., Saha, M., Almeida, J.S. and Oliveira, A.L., 2024. Multiple instance learning for WSI: A comparative analysis of attention-based approaches. Journal of Pathology Informatics, p. 100403. (Source Code: https://github.com/timafonso/WSI_MIL_ROI).
• Rafet, S.M., Lee, C., Griffin, K.T., Saha, M., Lee, C. and Mille, M.M., 2025. Realistic extension of partial-body pediatric CT for whole-body organ dose estimation in radiotherapy patients. Radiation Physics and Chemistry, 226, p. 112194.
• Saha, M., Jung, J.W., Lee, S.W., Lee, C., Lee, C. and Mille, M.M., 2023. A deep learning segmentation method to assess dose to organs at risk during breast radiotherapy. Physics and Imaging in Radiation Oncology, 28, p. 100520.
• Sadasivuni, S., Saha, M., Bhanushali, S.P., Banerjee, I. and Sanyal, A., 2023. In-sensor artificial intelligence and fusion with electronic medical records for at-home monitoring. IEEE Transactions on Biomedical Circuits and Systems, 17(2), pp. 312–322.
• Saha, M., Amin, S.B., Sharma, A., Kumar, T.S. and Kalia, R.K., 2022. AI-driven quantification of ground glass opacities in lungs of COVID-19 patients using 3D computed tomography imaging. PLOS ONE, 17(3), p. e0263916. (Source Code: https://github.com/sharmalab/GGOs_COVID-19).
• Verma, A., Amin, S.B., Naeem, M. and Saha, M., 2022. Detecting COVID-19 from chest computed tomography scans using AI-driven android application. Computers in Biology and Medicine, 143, p. 105298. (Source Code: https://github.com/monjoybme/CovCT_application).
• Sadasivuni, S., Saha, M., Bhatia, N., Banerjee, I. and Sanyal, A., 2022. Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset. Scientific Reports, 12(1), pp. 1–11. (Authors Sadasivuni, S. and Saha, M. contributed equally.) (Source Code: https://github.com/monjoybme/Sepsis_Classification_GUI).
• Saha, M., Guo, X. and Sharma, A., 2021. TilGAN: GAN for facilitating tumor-infiltrating lymphocyte pathology image synthesis with improved image classification. IEEE Access, 9, p. 79829. (Source Code: https://github.com/monjoybme/TilGAN-v1.0).
• Saha, M., Arun, I., Ahmed, R., Chatterjee, S. and Chakraborty, C., 2020. HscoreNet: A deep network for estrogen and progesterone scoring using breast IHC images. Pattern Recognition, p. 107200.
• Saha, M. and Chakraborty, C., 2018. Her2Net: A deep framework for semantic segmentation and classification of cell membranes and nuclei in breast cancer evaluation. IEEE Transactions on Image Processing, 27(5), pp. 2189–2200.
• Saha, M., Chakraborty, C. and Racoceanu, D., 2018. Efficient deep learning model for mitosis detection using breast histopathology images. Computerized Medical Imaging and Graphics, 64, pp. 29–40.
• Saha, M., Chakraborty, C., Arun, I., Ahmed, R. and Chatterjee, S., 2017. An advanced deep learning approach for Ki-67 stained hotspot detection and proliferation rate scoring for prognostic evaluation of breast cancer. Scientific Reports, 7(1), p. 3213.
• Saha, M., Arun, I., Agarwal, S., Ahmed, R., Chatterjee, S. and Chakraborty, C., 2017. Imprint cytology-based breast malignancy screening: An efficient nuclei segmentation technique. Journal of Microscopy, 268(2), pp. 155–171.
• Saha, M., Arun, I., Basak, B., Agarwal, S., Ahmed, R., Chatterjee, S., Bhargava, R. and Chakraborty, C., 2016. Quantitative microscopic evaluation of mucin areas and its percentage in mucinous carcinoma of the breast using tissue histological images. Tissue and Cell, 48(3), pp. 265–273.
• Saha, M., Mukherjee, R. and Chakraborty, C., 2016. Computer-aided diagnosis of breast cancer using cytological images: A systematic review. Tissue and Cell, 48(5), pp. 461–474.
• Banerjee, S., Saha, M., Arun, I., Basak, B., Agarwal, S., Ahmed, R., Chatterjee, S., Mahanta, L.B. and Chakraborty, C., 2017. Near-set-based mucin segmentation in histopathology images for detecting mucinous carcinoma. Journal of Medical Systems, 41(9), p. 144.
• Mukherjee, R., Saha, M., Routray, A. and Chakraborty, C., 2015. Nanoscale surface characterization of human erythrocytes by atomic force microscopy: A critical review. IEEE Transactions on NanoBioscience, 14(6), pp. 625–633.

PEER-REVIEWED CONFERENCE PAPERS

• Sadasivuni, S., Saha, M., Bhanushali, S.P., Banerjee, I. and Sanyal, A., 2022, May. Real-time sepsis prediction using fusion of on-chip analog classifier and electronic medical record. In IEEE International Symposium on Circuits and Systems (ISCAS), Austin, TX, USA, pp. 1635–1639.
• Saha, M., Agarwal, S., Arun, I., Ahmed, R., Chatterjee, S., Mitra, P. and Chakraborty, C., 2015. Histogram-based thresholding for automated nucleus segmentation using breast imprint cytology. In Advancements of Medical Electronics (pp. 49–57). Springer, New Delhi.
• Saha, M., Ray, A.K. and Basu, S.K., 2012. 3D CA model of tumor-induced angiogenesis. In International Conference on Modeling and Simulation of Diffusive Processes and Applications (ICMSDPA12), pp. 177–181.

PEER-REVIEWED CONFERENCE ABSTRACTS

• Saha, M., Tran, T.V.T., Hoang, P.H., Bhawsar, P.M., Homer, R., Baine, M.K., Sholl, L.M., Joubert, P., Leduc, C., Travis, W.D., Pfeiffer, R.M., Almeida, J.S., Yang, S.R. and Landi, M.T. (2026). Deep learning of H&E slides adds prognostic value beyond IASLC grading in non-mucinous lung adenocarcinoma among never-smokers. In: AACR Annual Meeting 2026. (Accepted for Presentation)
• Tran, T.V.T., Saha, M., Hoang, P.H., Bhawsar, P.M.S., Homer, R., Baine, M.K., Sholl, L.M., Joubert, P., Leduc, C., Travis, W.D., Pfeiffer, R.M., Almeida, J.S., Yang, S.R. and Landi, M.T., 2026. Histological profile of lung cancer in people who have never smoked and prognostic assessment of non-mucinous adenocarcinoma. In: AACR Annual Meeting 2026. (Accepted for Presentation)
• Saha, M., Zhang, T., Bhawsar, P.M., Zhao, W., Shi, J., Yang, S.R., Almeida, J.S. and Landi, M.T., 2025. A multimodal deep learning approach to predict survival in never-smoking lung cancer patients. Cancer Research, 85(8_Supplement_1), pp. 5017–5017.
• Zhao, W., Zhang, T., Hua, X., Hoang, P.H., Miraftab, M., Saha, M., McElderry, J.P., Sang, J., Lee, O., Hartman, C. and Khandekar, A., 2025. Abstract LB263: A transcriptomic signature predicts mortality risk and immune checkpoint inhibitor response beyond molecular and morphological features in lung adenocarcinoma from never smokers. Cancer Research, 85(8_Supplement_2), pp. LB263–LB263.
• Saha, M., Jung, J.W., Gopalakrishnan, M., John, K., Lee, C. and Mille, M., 2024. A deep learning-based method for predicting out-of-field radiotherapy dose for pediatric Wilms tumor patients. International Journal of Radiation Oncology, Biology, Physics, 120(2), pp. e654–e655.
• Saha, M., Zhang, T., Bhawsar, P., Zhao, W., Shi, J., Yang, S.R., Almeida, J. and Landi, M.T., 2024. Abstract LB243: Deep learning-based molecular characterization of lung cancers from never smokers using hematoxylin and eosin-stained whole slide images. Cancer Research, 84(7_Supplement), pp. LB243–LB243.
• Saha, M., Abubakar, M., Rohan, T.E., Pfeiffer, R.M., Duggan, M.A., Richert-Boe, K., Figueroa, J.D., Almeida, J.S. and Gierach, G.L., 2023. Comparison of deep learning approaches applied to hematoxylin and eosin-stained whole slide images from women with benign breast disease to predict risk of developing invasive breast cancer. Cancer Research, 83(7_Supplement), pp. 5393–5393.
• Saha, M., Jung, J., Kenworthy, W., Lee, C., Lee, C. and Mille, M., 2023. 3D deep neural network to improve the quality and efficiency of volumetric auto-segmentation of multiple organs for evaluation of dose to organs at risk during breast radiotherapy. In AAPM Annual Meeting 2023.
• Lee, S.W., Mundis, M., Vadnais, P., Mossahebi, S., Xu, H., Gopal, A., Mille, M., Lee, C., Saha, M. and Cheston, S., 2023, July. Dosimetric evaluation of critical organ doses in breast radiotherapy based on patient positioning and treatment modality: Photon (supine and prone) vs. proton supine. In AAPM Annual Meeting 2023.
• Saha, M., Jung, J., Lee, S., Lee, C. and Mille, M., 2022, June. Automatic multi-organ segmentation using a deep neural network for assessing dose to organs at risk during breast radiotherapy. Medical Physics, 49(6), pp. E198–E199.
• Saha, M., Arun, I. and Chakraborty, C., 2017. HerNet: An automated HER-2 scoring tool for breast cancer screening using deep learning. In 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17).
• Saha, M., Mukherjee, R., Arun, I., Chatterjee, S. and Chakraborty, C., 2016. Computer-aided detection of diagnostically relevant tubule regions in H&E-stained breast cancer histopathology images. Journal of Carcinogenesis, 15(Supplement), p. S33.

Professor Saha teaches courses to undergraduate, postgraduate and doctoral students at Ahmedabad University. His teaching uses experiential learning, problem-based learning, interdisciplinary and multidisciplinary approaches, co-teaching and ENABLE pedagogies. He has designed curricula for several doctoral courses, with more in development. He is also building courses for the Master of Science in Health Data Science, which launches in July 2027. These cover core areas of the field, including artificial intelligence, machine learning, deep learning, computer vision, natural language processing and genomics. In the classroom, he puts humility first, which he believes makes for better learning.

Courses taught, currently taught, or approved for upcoming semesters:

• CSE517 Python Programming
• EPI706 Data Science for Health
• FDP101 Democracy and Justice Studio (University Foundation)
• HDS642 Deep Learning Foundations for Healthcare

Professor Saha has been invited each year since 2021 to teach graduate students at the Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC. He designs and delivers lectures and hands-on laboratory sessions in biomedical image computing, imaging informatics and applied artificial intelligence. His teaching connects methods to real clinical and research data, using active learning and case-based problem solving.

Professor Saha serves on the editorial boards of leading journals in artificial intelligence and medical informatics, and he reviews manuscripts for journals in medical imaging, artificial intelligence, public health, epidemiology, cancer and biomedical engineering.

• Associate Editor, IEEE Transactions on Artificial Intelligence (2023 to present)
• Associate Editor, Pattern Analysis and Applications (2023 to present)
• Editorial Board Member, BMC Medical Informatics and Decision Making (2022 to present)
• Founding Editorial Board Member, BMC Artificial Intelligence (2024 to present)
• Advisory Board Member, Heliyon Medical Imaging (2024 to present)

Professor Saha has served in leadership, program committee and organizing roles for international conferences, workshops and symposia.

• Area Chair, MICCAI (2025)
• Program Committee Member, IEEE BigDataService / CISOSE (2025)
• Organizing Committee, NIH Career Symposium (2023)
• Program Committee Member, IEEE Big Data Computing, Service and Machine Learning Applications (2022 to 2023)
• Organizing Committee, Fellows Symposium, NCI/NIH (2022)
• Program Committee Member, International Workshop on Conceptual Modeling for Life Sciences (2022)
• Technical Program Committee Member, International Conference on Biomedical Engineering and Biotechnology (2020 to 2022)
• Technical Program Committee Member, International Conference on Electronics, Communication, Control Systems, and Computing Technologies (2021)

Research Awards

• NCI Director’s Award, National Cancer Institute, NIH (2023)
• DCEG Fellows Award for Research Excellence, NCI/NIH (2024)
• Fellows Award for Research Excellence (FARE), NIH (2022, 2025)
• DCEG Outstanding Paper Award, NCI/NIH (2026)

Mentorship Recognition

• Summer Research Mentor Award, NIH (2022, 2023, 2025)
• Amgen Scholar Mentor Award, NIH (2023)

Editorial and Peer-Review Recognition

• Outstanding Associate Editor, IEEE Transactions on Artificial Intelligence (2024)
• Distinguished Reviewer Award, IEEE Transactions on Medical Imaging (2023)
• Outstanding Reviewer Award, Informatics in Medicine Unlocked (2018)

International Fellowships

• Newton-Bhabha PhD Placement Fellowship, British Council, UK; host: University of Warwick, UK (2017)
• Raman-Charpak Fellowship, Government of France; host: Université Pierre et Marie Curie, France (2016)
• INSPIRE Fellowship, Government of India (2014 to 2019)

Academic Excellence

• Gold Medal, First Rank in MTech Biomedical Engineering (2012)
• Jawaharlal Nehru Memorial Award