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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 2.0: 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…