Recent developments in data science in general and machine learning in particular have
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …

Since 2010, substantial progress has been made in artificial intelligence (AI) and its
application to medicine. AI is explored in gastroenterology for endoscopic analysis of …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

Encoder-decoder networks are state-of-the-art approaches to biomedical image
segmentation, but have two problems: ie, the widely used pooling operations may discard …

Semantic image segmentation is the process of labeling each pixel of an image with its
corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a …

Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In
practice, it is difficult to find annotated medical images with corresponding segmentation …

Abstract In recent years Deep Learning has brought about a breakthrough in Medical Image
Segmentation. In this regard, U-Net has been the most popular architecture in the medical …

Computer-aided detection, localization, and segmentation methods can help improve
colonoscopy procedures. Even though many methods have been built to tackle automatic …

Colonoscopy is considered the gold standard for detection of colorectal cancer and its
precursors. Existing examination methods are, however, hampered by high overall miss …

Identifying polyps is challenging for automatic analysis of endoscopic images in computer-
aided clinical support systems. Models based on convolutional networks (CNN) …