Rajarsi Gupta

 

Rajarsi Gupta, MD, PhD     

Rajarsi Gupta, MD, PhD, Assistant Professor

Department of Biomedical Informatics
Instructor of Clinical Pathology
MART 7M-0806
Stony Brook, NY 11794
Phone: (631) 638-1330
Email: Rajarsi.Gupta@stonybrookmedicine.edu

INTERESTS

Medical Physics, Molecular Biophysics, and Biochemistry with an emphasis on spectroscopic molecular methods. Current work in Artificial Intelligence and Machine-Learning applications.
 
BIOGRAPHY
 
Dr. Rajarsi Gupta received his BA in Molecular Biophysics from the University of Pennsylvania, where he was a Benjamin Franklin Scholar, and earned his MD and PhD from the University of Illinois College of Medicine. He completed his residency in Anatomic and Clinical Pathology and a fellowship in Hematopathology at Stony Brook Medicine, followed by a fellowship in Biomedical and Pathology Informatics. He is board certified in Anatomic Pathology, Clinical Pathology, and Hematopathology, and he joined the Department of Biomedical Informatics at Stony Brook University as a member of the faculty.
 
RESEARCH
 
Dr. Gupta’s research applies machine learning and computer vision to develop quantitative image analysis methods for Digital Pathology. His work centers on scalable Pathomics pipelines that characterize tumor-immune interactions in cancer, including deep learning methods to map tumor-infiltrating lymphocytes (TILs) across whole slide images at population scale, nuclear segmentation and cell classification, multiplex immunohistochemistry analysis, and the study of how pathologists visually examine tissue. He collaborates on efforts to make computational pathology reproducible and clinically usable, including open-source tooling, curated pathologist-annotated validation datasets, and international working group efforts on the standardization and regulatory qualification of image-based immune biomarkers. The goal of this work is to translate quantitative tissue measurements into specialized clinical applications for Precision Medicine and immunotherapy.
 
SELECTED PUBLICATIONS

Full list: Google Scholar | ORCID   

Digital Pathology, Pathomics, and the Tumor Immune Microenvironment

  1. Joel Saltz, Rajarsi Gupta, Le Hou, Tahsin Kurc, Pankaj Singh, Vu Nguyen, Dimitris Samaras, Kenneth R. Shroyer, et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Reports, 23(1):181-193, 2018.
  2. Shahira Abousamra, Rajarsi Gupta, Le Hou, Rebecca Batiste, Tianhao Zhao, Anand Shankar, Arvind Rao, Chao Chen, et al. Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer. Frontiers in Oncology, 11:806603, 2022.
  3. Han Le, Rajarsi Gupta, Le Hou, Shahira Abousamra, Danielle Fassler, Luke Torre-Healy, Richard A. Moffitt, Tahsin Kurc, et al. Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer. The American Journal of Pathology, 190(7):1491-1504, 2020.
  4. Michael R. Moore, Isabel D. Friesner, Emanuelle M. Rizk, Benjamin T. Fullerton, Manas Mondal, Megan H. Trager, Karen Mendelson, Rajarsi Gupta, et al. Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma. Scientific Reports, 11:2809, 2021.
  5. Rajarsi Gupta, Han Le, John Van Arnam, David Belinsky, Mahmudul Hasan, Dimitris Samaras, Tahsin Kurc, Joel H. Saltz. Characterizing Immune Responses in Whole Slide Images of Cancer With Digital Pathology and Pathomics. Current Pathobiology Reports, 8:157-167, 2020.
  6. Rajarsi Gupta, Tahsin Kurc, Ashish Sharma, Jonas S. Almeida, Joel Saltz. The Emergence of Pathomics. Current Pathobiology Reports, 7:73-84, 2019.
  7. David J. Foran, Eric B. Durbin, Wenjin Chen, Evita Sadimin, Ashish Sharma, Imon Banerjee, Tahsin Kurc, Rajarsi Gupta, et al. An expandable informatics framework for enhancing central cancer registries with digital pathology specimens, computational imaging tools, and advanced mining capabilities. Journal of Pathology Informatics, 13:100167, 2022.
  8. Jakub R. Kaczmarzyk, Sarah C. Van Alsten, Alyssa J. Cozzo, Rajarsi Gupta, Peter K. Koo, Melissa A. Troester, Katherine A. Hoadley, Joel H. Saltz. Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models. npj Digital Medicine, 2025.

Machine Learning and Computer Vision for Histopathology

  1. Le Hou, Vu Nguyen, Ariel B. Kanevsky, Dimitris Samaras, Tahsin M. Kurc, Tianhao Zhao, Rajarsi R. Gupta, Yi Gao, et al. Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images. Pattern Recognition, 86:188-200, 2019.
  2. Le Hou, Ayush Agarwal, Dimitris Samaras, Tahsin M. Kurc, Rajarsi R. Gupta, Joel H. Saltz. Robust Histopathology Image Analysis: To Label or to Synthesize?. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
  3. Shahira Abousamra, David Belinsky, John Van Arnam, Felicia Allard, Eric Yee, Rajarsi Gupta, Tahsin Kurc, Dimitris Samaras, et al. Multi-Class Cell Detection Using Spatial Context Representation. IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
  4. Shahira Abousamra, Rajarsi Gupta, Tahsin Kurc, Dimitris Samaras, Joel Saltz, Chao Chen. Topology-Guided Multi-Class Cell Context Generation for Digital Pathology. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
  5. Saarthak Kapse, Pushpak Pati, Srijan Das, Jingwei Zhang, Chao Chen, Maria Vakalopoulou, Joel Saltz, Rajarsi R. Gupta, et al. SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
  6. Saarthak Kapse, Srijan Das, Jingwei Zhang, Rajarsi R. Gupta, Joel Saltz, Dimitris Samaras, Prateek Prasanna. Attention De-sparsification Matters: Inducing diversity in digital pathology representation learning. Medical Image Analysis, 93:103070, 2024.
  7. Souradeep Chakraborty, Ruoyu Xue, Rajarsi Gupta, Oksana Yaskiv, Constantin Friedman, Natallia Sheuka, Dana Perez, Paul Friedman, et al. Measuring and predicting where and when pathologists focus their visual attention while grading whole slide images of cancer. Medical Image Analysis, 2025.
  8. Kevin Thandiackal, Luigi Piccinelli, Rajarsi Gupta, Pushpak Pati, Orcun Goksel. Multi-Scale Feature Alignment for Continual Learning of Unlabeled Domains. IEEE Transactions on Medical Imaging, 43(7):2599-2609, 2024.
  9. Shahira Abousamra, Danielle Fassler, Rajarsi Gupta, Tahsin Kurc, Luisa F. Escobar-Hoyos, Dimitris Samaras, Kenneth R. Shroyer, Joel Saltz, et al. Label-Efficient Deep Color Deconvolution of Brightfield Multiplex IHC Images. IEEE Transactions on Medical Imaging, 2025.
  10. Danielle J. Fassler, Shahira Abousamra, Rajarsi Gupta, Chao Chen, Maozheng Zhao, David Paredes, Syeda Areeha Batool, Beatrice S. Knudsen, et al. Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images. Diagnostic Pathology, 15:100, 2020.
  11. Le Hou, Rajarsi Gupta, John S. Van Arnam, Yuwei Zhang, Kaustubh Sivalenka, Dimitris Samaras, Tahsin M. Kurc, Joel H. Saltz. Dataset of segmented nuclei in hematoxylin and eosin stained histopathology images of ten cancer types. Scientific Data, 7:185, 2020.
  12. Quoc Dang Vu, Simon Graham, Tahsin Kurc, Minh Nguyen Nhat To, Muhammad Shaban, Talha Qaiser, Navid Alemi Koohbanani, Rajarsi Gupta, et al. Methods for Segmentation and Classification of Digital Microscopy Tissue Images. Frontiers in Bioengineering and Biotechnology, 7:53, 2019.
  13. Jakub R. Kaczmarzyk, Alan O’Callaghan, Fiona Inglis, Swarad Gat, Tahsin Kurc, Rajarsi Gupta, Erich Bremer, Peter Bankhead, et al. Open and reusable deep learning for pathology with WSInfer and QuPath. npj Precision Oncology, 8:9, 2024.
  14. Jakub R. Kaczmarzyk, Rajarsi Gupta, Tahsin M. Kurc, Shahira Abousamra, Joel H. Saltz, Peter K. Koo. ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification. Computer Methods and Programs in Biomedicine, 239:107631, 2023.

Standards, Validation, and Regulatory Science for Computational Pathology

  1. Jeppe Thagaard, Glenn Broeckx, David B Page, Chowdhury Arif Jahangir, Sara Verbandt, Zuzana Kos, Rajarsi Gupta, Reena Khiroya, et al. Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer. The Journal of Pathology, 260(5):498-513, 2023. (International Immuno-Oncology Biomarker Working Group on Breast Cancer)
  2. David B Page, Glenn Broeckx, Chowdhury Arif Jahangir, Sara Verbandt, Rajarsi R Gupta, Jeppe Thagaard, Reena Khiroya, Zuzana Kos, et al. Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer. The Journal of Pathology, 260(5):514-532, 2023. (International Immuno-Oncology Biomarker Working Group on Breast Cancer)
  3. Chowdhury Arif Jahangir, David B Page, Glenn Broeckx, Claudia A Gonzalez, Caoimbhe Burke, Clodagh Murphy, Jorge S Reis‐Filho, Rajarsi R Gupta, et al. Image‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer. The Journal of Pathology, 262(3):271-288, 2024. (International Immuno-Oncology Biomarker Working Group on Breast Cancer)
  4. Steven Hart, Victor Garcia, Sarah N Dudgeon, Matthew G Hanna, Xiaoxian Li, Kim RM Blenman, Katherine Elfer, Rajarsi Gupta, et al. Initial interactions with the FDA on developing a validation dataset as a medical device development tool. The Journal of Pathology, 261(4):378-384, 2023.
  5. Amy Ly, Victor Garcia, Kim R M Blenman, Anna Ehinger, Katherine Elfer, Matthew G Hanna, Xiaoxian Li, Rajarsi Gupta, et al. Training pathologists to assess stromal tumour‐infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research. Histopathology, 85(1):133-152, 2024.
  6. Sarah N. Dudgeon, Si Wen, Matthew G. Hanna, Rajarsi Gupta, Mohamed Amgad, Manasi Sheth, Hetal Marble, Richard Huang, et al. A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study. Journal of Pathology Informatics, 12:45, 2021.

Hematopathology and Diagnostic Pathology

  1. Spencer Krichevsky, Yuwei Zhang, Madhu M Ouseph, Tahmeena Ahmed, Ghaith Abu-Zeinah, Joseph M. Scandura, Rajarsi Gupta. Deep Learning Predicts JAK2, Calr TET2, and ASXL1 directly from Whole Slide Marrow Images. Blood, 144(Supplement 1), 2024. (American Society of Hematology Annual Meeting)
  2. Spencer Krichevsky, Madhu M Ouseph, Yuwei Zhang, Ghaith Abu-Zeinah, Joseph M. Scandura, Rajarsi Gupta. A Deep Learning-Based Pathomics Methodology for Quantifying and Characterizing Nucleated Cells in the Bone Marrow Microenvironment. Blood, 142(Supplement 1), 2023. (American Society of Hematology Annual Meeting)
  3. Kester Haye, Sruthi Babu, Lyanne Oblein, Rajarsi Gupta, Ali Akalin, Luisa F. Escobar-Hoyos, Kenneth R. Shroyer. Keratin 17 Expression Predicts Poor Clinical Outcome in Patients With Advanced Esophageal Squamous Cell Carcinoma. Applied Immunohistochemistry & Molecular Morphology, 29(6):399-406, 2020.
  4. Kester Haye, Rajarsi Gupta, Christopher Metter, Jingxuan Liu. Clinical Applications for Immunohistochemistry of Breast Lesions. Methods in Molecular Biology, 1406:31-48, 2016.
  5. Ammar A. Chaudhry, Kevin S. Baker, Elaine S. Gould, Rajarsi Gupta. Necrotizing Fasciitis and Its Mimics: What Radiologists Need to Know. American Journal of Roentgenology, 204(1):128-139, 2015.

Biomedical Optics and Near-Infrared Spectroscopy

  1. Ursula Wolf, Vladislav Toronov, Jee H. Choi, Rajarsi Gupta, Antonios Michalos, Enrico Gratton, Martin Wolf. Correlation of functional and resting state connectivity of cerebral oxy-, deoxy-, and total hemoglobin concentration changes measured by near-infrared spectrophotometry. Journal of Biomedical Optics, 16(8):087013, 2011.
  2. Christopher O. Olopade, Edward Mensah, Rajarsi Gupta, Dezheng Huo, Daniel L. Picchietti, Enrico Gratton, Antonios Michalos. Noninvasive Determination of Brain Tissue Oxygenation during Sleep in Obstructive Sleep Apnea: A Near-Infrared Spectroscopic Approach. Sleep, 30(12):1747-1755, 2007.
  3. JeeHyun Choi, Martin Wolf, Vladislav Toronov, Ursula Wolf, Chiara Polzonetti, Dennis Hueber, Larisa P. Safonova, Rajarsi Gupta, et al. Noninvasive determination of the optical properties of adult brain: near-infrared spectroscopy approach. Journal of Biomedical Optics, 9(1):221-229, 2004.
  4. Vlad Toronov, Scott Walker, Rajarsi Gupta, Jee H Choi, Enrico Gratton, Dennis Hueber, Andrew Webb. The roles of changes in deoxyhemoglobin concentration and regional cerebral blood volume in the fMRI BOLD signal. NeuroImage, 19(4):1521-1531, 2003.