Faculty

Monjoy Saha

Assistant Professor, Bagchi School of Public Health

PhD (IIT Kharagpur)

+91.79.61911281

Research Interests: Public Health Data Science, Computational Epidemiology, Cancer Epidemiology, Digital Pathology for Population Health, Artificial Intelligence for Disease Risk Prediction and Early Detection, Genomic and Molecular Data Analysis, Electronic Health Record-Linked Outcomes Research, Health Informatics, Infectious Disease

Profile

Professor Saha’s research bridges public health data science, computational epidemiology, and biomedical informatics, with a primary focus on cancer. He brings together methods from artificial intelligence, digital pathology, radiology, genomics, and electronic health record research to study how disease develops, progresses, and can be detected and prevented at the population level.

Professor Saha was a Research Fellow in the Division of Cancer Epidemiology and Genetics (DCEG) at the National Cancer Institute (NCI), National Institutes of Health (NIH), USA, one of the world’s foremost cancer research centres. Earlier, he held the position of Research Scientist in the Department of Biomedical Informatics at Emory University, USA. Across these positions, he worked in epidemiology, pathology, radiology, medical imaging, genomics, and health data science.

Professor Saha completed his PhD in Medical Science and Technology from the Indian Institute of Technology (IIT) Kharagpur, where his doctoral research focused on computer-assisted detection and evaluation of breast cancer using digital pathology. He holds an MTech in Biomedical Engineering from IIT (BHU) Varanasi, where he received the Gold Medal, and a BTech in Biomedical Engineering from West Bengal University of Technology. During his doctoral training, he held the Innovation in Science Pursuit for Inspired Research (INSPIRE) Fellowship from the Department of Science and Technology (DST), Government of India; the Raman-Charpak Fellowship (IFCPAR/CEFIPRA); and the Newton-Bhabha PhD Placement Fellowship (British Council and DST), which opened the path to achieve international research training at Université Pierre et Marie Curie, France, and the University of Warwick, UK.

A central strand of Professor Saha’s research is cancer patho-epidemiology: understanding how information from routine pathology images, molecular data, and clinical records can illuminate cancer risk and tumour behaviour across populations. His work has demonstrated how deep learning applied to benign breast biopsy images can identify women at elevated risk of future invasive breast cancer, for which he received the DCEG Outstanding Paper Award for a publication in JNCI Cancer Spectrum. In parallel, he contributes to radiation epidemiology research, including the development of computational methods to estimate radiation doses to organs at risk during radiotherapy and the epidemiological study of long-term cancer outcomes in survivors treated with radiation. More broadly, his research spans lung cancer genomics, never-smoker lung cancer characterisation, and multimodal predictive modelling integrating imaging, genomic, and clinical data.

Professor Saha has received numerous recognitions, including the NCI Director’s Award, the DCEG Fellows Award for Research Excellence, multiple NIH Fellows Awards for Research Excellence (FARE), NIH Intramural Informatics Tool Challenge Awards, the NIH Summer Research Mentor Award, and the Outstanding Associate Editor Award from IEEE Transactions on Artificial Intelligence. He serves as Associate Editor for IEEE Transactions on Artificial Intelligence and Pattern Analysis and Applications, and as an Editorial Board Member of BMC Medical Informatics and Decision Making and BMC Artificial Intelligence.

At Ahmedabad University, Professor Saha aims to collaborate in building a research and teaching programme in public health data science and computational epidemiology. He is particularly interested in applying these approaches to disease challenges relevant to India and South Asia — spanning cancer, infectious diseases, and chronic conditions. He will also work in fostering interdisciplinary collaborations across the University’s schools of public health, engineering, life sciences, social sciences, and management.

Research

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.

Publications

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.

Teaching

Previous teaching experience: Professor Saha has delivered graduate-level instruction in Imaging Informatics as an invited guest lecturer at Georgetown University. He has also taught project-based modules in machine learning, biomedical signal processing, computer vision and predictive modelling in healthcare at Emory University and served as a Teaching Assistant at the Indian Institute of Technology Kharagpur for laboratory and tutorial sessions in biostatistics and Pattern Analysis in Medicine.