4 papers · 1 filter
TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis
Chaeyeon Lee, Khang Nguyen Quoc, Jinsol Song +3
Whole slide image (WSI) analysis is central to computational pathology, with multiple instance learning (MIL) emerging as the standard pipeline for slide-level diagnosis. However,…
Hierarchical Classification for Improved Histopathology Image Analysis
Keunho Byeon, Jinsol Song, Seong Min Hong +2
Whole-slide image analysis is essential for diagnostic tasks in pathology, yet existing deep learning methods primarily rely on flat classification, ignoring hierarchical relations…
Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images
Jinsol Song, Jiamu Wang, Anh Tien Nguyen +4
Anomaly detection in computational pathology aims to identify rare and scarce anomalies where disease-related data are often limited or missing. Existing anomaly detection methods,…
USegMix: Unsupervised Segment Mix for Efficient Data Augmentation in Pathology Images
Jiamu Wang, Jin Tae Kwak
In computational pathology, researchers often face challenges due to the scarcity of labeled pathology datasets. Data augmentation emerges as a crucial technique to mitigate this l…