6 papers
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,…
Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis
Jiamu Wang, Keunho Byeon, Jinsol Song +4
Anomaly detection is an emerging approach in digital pathology for its ability to efficiently and effectively utilize data for disease diagnosis. While supervised learning approach…
NucleiMix: Realistic Data Augmentation for Nuclei Instance Segmentation
Jiamu Wang, Jin Tae Kwak
Nuclei instance segmentation is an essential task in pathology image analysis, serving as the foundation for many downstream applications. The release of several public datasets ha…
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…