3 papers
cs.CV2026
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…
cs.LG2025
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…
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…