Rajarsi Gupta MD PhD
Hematopathology, Anatomic Pathology, Clinical Pathology
Physician
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Department Of Biomedical informatics, HSC L3-043Stony Brook University Medical CenterStony Brook, NY 11794
Phone+1 217-721-1377
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Summary
- Dr. Rajarsi Gupta is a pathologist based in Stony Brook, NY, with subspecialties in hematopathology, anatomic pathology, and clinical pathology. He completed his MD/PhD at the University of Illinois College of Medicine in 2010, followed by a residency and fellowship at Stony Brook Medicine/University Hospital. Since 2017, he has been serving as an Instructor of Clinical Pathology and Biomedical-Pathology Informatics Fellow at Stony Brook University Health Sciences Center School of Medicine. Dr. Gupta has contributed to several scientific publications, including topics like deep learning in pathology and tumor prediction, with notable works featured in the IEEE Computer Society Conference on Computer Vision and Pattern Recognition and NPJ Precision Oncology.
Education & Training
Stony Brook Medicine/University HospitalFellowship, Hematopathology, 2015 - 2016- Stony Brook Medicine/University HospitalResidency, Pathology-Anatomic and Clinical, 2011 - 2015
University of Illinois College of MedicineClass of 2010, MD/PhD (Medical Scholars Program)
University of PennsylvaniaBA, Molecular Biophysics, Benjamin Franklin Scholar, 1996 - 1999
Certifications & Licensure
NY State Medical License 2021 - 2026
Publications & Presentations
PubMed
- Uncertainty Estimation for Tumor Prediction with Unlabeled Data.Juyoung Yun, Shahira Abousamra, Chen Li, Rajarsi Gupta, Tahsin Kurc
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops. 2024-06-01 - SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology.Saarthak Kapse, Pushpak Pati, Srijan Das, Jingwei Zhang, Chao Chen
Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2024-06-01 - 4 citationsTraining pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.Amy Ly, Victor Garcia, Kim R M Blenman, Anna Ehinger, Katherine Elfer
Histopathology. 2024-05-01
Journal Articles
- Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast CancerRajarsi Gupta, Joel Saltz, The American Journal of Pathology
Press Mentions
- Automated Digital TIL Analysis (ADTA) Adds Prognostic Value to Standard Assessment of Depth and Ulceration in Primary MelanomaFebruary 2nd, 2021
Deep Learning-Based Image Analysis Methods for Brightfield-Acquired Multiplex Immunohistochemistry ImagesJuly 28th, 2020
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