Ruchika Verma

Mount Sinai Health System — United States

Medicine · Health Sciences

14h-index2.5kcitations80works3.32yr avg
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Research Topics

Radiomics and Machine Learning in Medical Imaging(49), AI in cancer detection(29), Glioma Diagnosis and Treatment(16), Molecular Biology Techniques and Applications(9), MRI in cancer diagnosis(7)

Publications76 total

Supplementary Data 1 from Learning Individual Survival Models from PanCancer Whole Transcriptome Data
2025Open Access
BIOM-104. AI-BASED QUANTIFICATION OF PD-L1 IMMUNOHISTOCHEMISTRY IN GLIOBLASTOMA: OVERCOMING INTERPRETATION CHALLENGES WITH MACHINE LEARNING
Neuro-Oncology·2025
Predicting Patient Self-reported Race From Skin Histological Images with Deep Learning
Lecture notes in computer science·2025
Supplementary Figure S2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Table S1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Figure S1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Table S2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
A clinical benchmark of public self-supervised pathology foundation models
Nature Communications·2025· 44 citedOpen Access
Supplementary Table S1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Table 2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Figure S2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Table S2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Figure 1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Table 1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Figure S1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Data from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Figure 2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
Cancer Research Communications·2024· 2 citedOpen Access
Risk of Ischemic Heart Disease in Women with Dysmenorrhea: A Longitudinal Analysis of 251,264 Patients Across Three Diverse Cohorts
2024· 1 citedOpen Access
Artificial intelligence and machine learning applications for cultured meat
Frontiers in Artificial Intelligence·2024· 25 citedOpen Access
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Frequent Co-authors

Pallavi Tiwari(17), Neeraj Kumar(16), Amit Sethi(13), Anant Madabhushi(10), Niha Beig(9), Ramón Correa(9), Volodymyr Statsevych(9), Peter H. Gann(9), Manmeet S. Ahluwalia(8), Nikhil Cherian Kurian(8), Kaustav Bera(7), Virginia Hill(7), Stephanie M. McGregor(7), Prateek Prasanna(6), Marwa Ismail(6), Jacob Antunes(4), Vinay Varadan(4), Russell Greiner(4), Anas Saeed Bamashmos(3), Jonathan Chen(3)