Gland Segmentation Via Dual Encoders and Boundary-Enhanced Attention

arXiv:2401.15990v2 Announce Type: replace-cross
Abstract: Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland shapes, severe deformation of malignant glands, and overlapping adhesions between glands. Gland segmentation has always been very challenging. To address these problems, we propose a DEA model. This model consists of two branches: the backbone encoding and decoding network and the local semantic extraction network. The backbone encoding and decoding network extracts …

abstract arxiv attention automated cs.cv cs.lg eess.iv however images segmentation type via