1 citations · 1 across the 4 of their papers we have counts for
4 papers
MINT: Molecularly Informed Training with Spatial Transcriptomics Supervision for Pathology Foundation Models
Minsoo Lee, Jonghyun Kim, Juseung Yun +2
Pathology foundation models learn morphological representations through self-supervised pretraining on large-scale whole-slide images, yet they do not explicitly capture the underl…
EXAONE Path 2.5: Pathology Foundation Model with Multi-Omics Alignment
Juseung Yun, Sunwoo Yu, Sumin Ha +4
Cancer progression arises from interactions across multiple biological layers, especially beyond morphological and across molecular layers that remain invisible to image-only model…
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…
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…