activity
20222026
most citedUncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

138 citations · 150 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

When Cases Get Rare: A Retrieval Benchmark for Off-Guideline Clinical Question Answering

Doeun Lee, Muge Zhang, Yi Yu +11

Across medical specialties, clinical practice is anchored in evidence-based guidelines that codify best studied diagnostic and treatment pathways. These pathways routinely fall sho…

cs.CV2026

PBSBench: A Multi-Level Vision-Language Framework and Benchmark for Hematopathology Whole Slide Image Interpretation

Yuanlong Wang, Weichi Chen, Adrian Rajab +4

Peripheral Blood Smear (PBS) is a critical microscopic examination in hematopathology that yields whole-slide imaging (WSI). Unlike solid tissue pathology, PBS interpretation focus…

q-bio.QM2023★ 8 cited

Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization

James M. Dolezal, Sara Kochanny, Emma Dyer +6

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environment…

eess.IV2022★ 4 cited

Deep Learning Generates Synthetic Cancer Histology for Explainability and Education

James M. Dolezal, Rachelle Wolk, Hanna M. Hieromnimon +20

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or excee…

q-bio.QM2022★ 138 cited

Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

James M Dolezal, Andrew Srisuwananukorn, Dmitry Karpeyev +13

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-wo…