RetSeg: Retention-based Colorectal Polyps Segmentation Network

arXiv:2310.05446v5 Announce Type: replace-cross
Abstract: Vision Transformers (ViTs) have revolutionized medical imaging analysis, showcasing superior efficacy compared to conventional Convolutional Neural Networks (CNNs) in vital tasks such as polyp classification, detection, and segmentation. Leveraging attention mechanisms to focus on specific image regions, ViTs exhibit contextual awareness in processing visual data, culminating in robust and precise predictions, even for intricate medical images. Moreover, the inherent self-attention mechanism in Transformers accommodates varying input sizes and resolutions, granting an unprecedented flexibility absent in …

abstract analysis arxiv attention attention mechanisms classification cnns convolutional neural networks cs.cv cs.lg data detection eess.iv focus image imaging medical medical imaging network networks neural networks processing retention robust segmentation tasks transformers type vision vision transformers visual visual data vital