1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2025
EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision
Myeongjang Pyeon, Janghyeon Lee, Minsoo Lee +7
In digital pathology, whole-slide images (WSIs) are often difficult to handle due to their gigapixel scale, so most approaches train patch encoders via self-supervised learning (SS…
eess.IV2025
FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation
Haolong Jin, Shenglin Liu, Cong Cong +4
Federated learning (FL) has emerged as a promising approach for collaborative medical image analysis, enabling multiple institutions to build robust predictive models while preserv…
cs.LG2024★ 1 cited
EXAONEPath 1.0 Patch-level Foundation Model for Pathology
Juseung Yun, Yi Hu, Jinhyung Kim +2
Recent advancements in digital pathology have led to the development of numerous foundational models that utilize self-supervised learning on patches extracted from gigapixel whole…