5 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…
HyperspectralMAE: The Hyperspectral Imagery Classification Model using Fourier-Encoded Dual-Branch Masked Autoencoder
Wooyoung Jeong, Hyun Jae Park, Seonghun Jeong +3
Hyperspectral imagery provides rich spectral detail but poses unique challenges because of its high dimensionality in both spatial and spectral domains. We propose \textit{Hyperspe…
ChatEXAONEPath: An Expert-level Multimodal Large Language Model for Histopathology Using Whole Slide Images
Sangwook Kim, Soonyoung Lee, Jongseong Jang
Recent studies have made significant progress in developing large language models (LLMs) in the medical domain, which can answer expert-level questions and demonstrate the potentia…