Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation

arXiv:2404.01127v1 Announce Type: new
Abstract: Accurate segmentation of lesion regions is crucial for clinical diagnosis and treatment across various diseases. While deep convolutional networks have achieved satisfactory results in medical image segmentation, they face challenges such as loss of lesion shape information due to continuous convolution and downsampling, as well as the high cost of manually labeling lesions with varying shapes and sizes. To address these issues, we propose a novel medical visual prompting (MVP) framework that leverages pre-training and …

abstract arxiv challenges clinical continuous convolution cs.ai cs.cv diagnosis diseases face framework image information loss medical mvp networks prompting quality results segmentation treatment type visual visual prompting