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Acta Neuropathologica Communications|October 22, 2021
Deep learning reveals disease-specific signatures of white matter pathology in tauopathiesAnthony R Vega, Rati Chkheidze, Vipul Jarmale, et al.Genome Medicine|September 19, 2025
MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphologyAleksandra Weronika Nielsen, Hafez Eslami Manoochehri, Hua Zhong, et al.Cancer Research|June 2, 2022
Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep LearningPaul H Acosta, Vandana Panwar, Vipul Jarmale, et al.Medrxiv : the Preprint Server for Health Sciences|December 3, 2025
Radiologic, Pathologic, and Deep Learning Predictors of Response to Immune Checkpoint Blockade in Renal Cell Carcinoma Patients Undergoing Post-Treatment NephrectomyPayal Kapur, Alana Christie, Vipul Jarmale, et al.Biorxiv : the Preprint Server for Biology|July 3, 2026
A Visually Interpretable Histopathology-Based Immune Model Predicts T-effector Biology and Response to Immune checkpoint inhibition in Clear Cell Renal Cell Carcinoma Clinical Trial and Contemporary Real-World DatasetsAveri Perny, Vipul Jarmale, Jay Jasti, et al.Arxiv|June 10, 2024
Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical TrialJay Jasti, Hua Zhong, Vandana Panwar, et al.Nature Communications|March 18, 2025
Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trialJay Jasti, Hua Zhong, Vandana Panwar, et al.Pageof 1