5 papers
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…
Patch Stitching Data Augmentation for Cancer Classification in Pathology Images
Jiamu Wang, Chang-Su Kim, Jin Tae Kwak
Computational pathology, integrating computational methods and digital imaging, has shown to be effective in advancing disease diagnosis and prognosis. In recent years, the develop…
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…