3 papers
cs.AI2026
Spatial proteomics guided by H&E-based AI reveals recurrence-risk niches in triple-negative breast cancer
Yesung Cho, Ji Hwan Park, Chanil Kim +27
Deep learning models can predict cancer recurrence from H&E stained slides, but the localized molecular states underlying these predictions remain largely obscured. Here, we develo…
cs.CV2026
Efficient AI-Driven Multi-Section Whole Slide Image Analysis for Biochemical Recurrence Prediction in Prostate Cancer
Yesung Cho, Dongmyung Shin, Sujeong Hong +5
Prostate cancer is one of the most frequently diagnosed malignancies in men worldwide. However, precise prediction of biochemical recurrence (BCR) after radical prostatectomy remai…
cs.CV2026
G2L:From Giga-Scale to Cancer-Specific Large-Scale Pathology Foundation Models via Knowledge Distillation
Yesung Cho, Sungmin Lee, Geongyu Lee +3
Recent studies in pathology foundation models have shown that scaling training data, diversifying cancer types, and increasing model size consistently improve their performance. Ho…