SegMix:Shuffle-based Feedback Learning for Semantic Segmentation of Pathology Images

ArXi:2604.15777v1 Announce Type: cross Segmentation is a critical task in computational pathology, as it identifies areas affected by disease or abnormal growth and is essential for diagnosis and treatment. However, acquiring high-quality pixel-level supervised segmentation data requires significant workload demands from experienced pathologists, limiting the application of deep learning. To overcome this challenge, relaxing the label conditions to image-level classification labels allows for data to be used and scenarios to be enabled.