The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Deep learning has emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …

Clinically, proper polyp localization in endoscopy images plays a vital role in the follow-up
treatment (eg, surgical planning). Deep convolutional neural networks (CNNs) provide a …

Recently, deep convolutional neural networks (CNNs) have provided us an effective tool for
automated polyp segmentation in colonoscopy images. However, most CNN-based …

Abstract Computer-Aided Diagnosis (CAD) systems for polyp detection provide essential
support for colorectal cancer screening and prevention. Recently, deep learning technology …

Accurate lesion segmentation based on endoscopy images is a fundamental task for the
automated diagnosis of gastrointestinal tract (GI Tract) diseases. Previous studies usually …

Colonoscopy is the gold standard for early diagnosis and pre-emptive treatment of colorectal
cancer by detecting and removing colonic polyps. Deep learning approaches to polyp …

Colorectal cancer is a major health problem, where advances towards computer-aided
diagnosis (CAD) systems to assist the endoscopist can be a promising path to improvement …

Deep Learning (DL) has attracted a lot of attention in the field of medical image analysis
because of its higher performance in image classification when compared to previous state …

Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early
detection and diagnosis, comprehensive assessment of treatment response, and precise …