1 citations · 1 across the 3 of their papers we have counts for
4 papers
SCALE: Synthetic Calibration via Agreement Labeling in Embedding Space
Wenjun Liu, Saeed Hassanpour
Foundation models for computational pathology are usually evaluated using AUC and accuracy, while calibration is often left untested. This matters because a model can be accurate o…
Cross-Modal Contrastive Learning from Histopathology and CT for Automated Renal Cell Carcinoma Grading
Amit Das, Tanmay Shukla, Naofumi Tomita +9
Background: Clear cell renal cell carcinoma (ccRCC) exhibits substantial clinical heterogeneity, and accurate grade assessment is essential for risk stratification and treatment pl…
Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images
Amit Das, Naofumi Tomita, Kyle J. Syme +6
Hematoxylin and Eosin (H&E) staining is a cornerstone of pathological analysis, offering reliable visualization of cellular morphology and tissue architecture for cancer diagnosis,…
A Benchmark for Long-Form Medical Question Answering
Pedram Hosseini, Jessica M. Sin, Bing Ren +4
There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA). Most existing medical QA evaluation benchmarks focus on auto…