Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets

arXiv:2403.19177v1 Announce Type: new
Abstract: Medical image segmentation presents the challenge of segmenting various-size targets, demanding the model to effectively capture both local and global information. Despite recent efforts using CNNs and ViTs to predict annotations of different scales, these approaches often struggle to effectively balance the detection of targets across varying sizes. Simply utilizing local information from CNNs and global relationships from ViTs without considering potential significant divergence in latent feature distributions may result in substantial information loss. To …

abstract annotations arxiv balance challenge cnns cs.ai cs.cv detection global image information loss medical segmentation struggle targets type