Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-t...
BACKGROUND AND STUDY AIMS: We introduced a new platform for performing colonoscopy with robotic steering and automated lumen centralization (RS-ALC) and evaluated its technical feasibility.
Recent achievement of the learning-based classification leads to the noticeable performance improvement in automatic polyp detection. Here, building large good datasets is very crucial for learning a reliable detector. However, it is practically chal...
The American journal of gastroenterology
Mar 10, 2015
BACKGROUND: An accurate system for tracking of colonoscopy quality and surveillance intervals could improve the effectiveness and cost-effectiveness of colorectal cancer (CRC) screening and surveillance. The purpose of this study was to create and te...
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Dec 25, 2025
Colorectal cancer typically originates from the malignant transformation of colonic polyps, making the automatic and accurate segmentation of colonic polyps crucial for clinical diagnosis. Deep learning techniques such as U-Net and Transformer can ef...
Scandinavian journal of gastroenterology
Jun 1, 2025
OBJECTIVE: Colon polyp detection is crucial in reducing the incidence of colorectal cancer. However, due to the diverse morphology of colon polyps, their high similarity to surrounding tissues, and the difficulty of detecting small target polyps, fal...
Polyps, like a silent time bomb in the gut, are always lurking and can explode into deadly colorectal cancer at any time. Many methods are attempted to maximize the early detection of colon polyps by screening, however, there are still face some chal...
This statement conveys the European Society of Gastrointestinal Endoscopy (ESGE) position on the use of computer-aided detection (CADe) with artificial intelligence (AI) during colonoscopy for colorectal cancer (CRC) screening or surveillance. The ES...
BACKGROUND AND AIMS: Insufficient bowel preparation accounts for up to 42% of missed adenomas in colonoscopy. However, major analysis programs found no correlation between adenoma detection rate and the human-rated Boston Bowel Preparation Scale (BBP...
IEEE journal of biomedical and health informatics
Jun 1, 2025
A major challenge in applying deep learning to medical imaging is the paucity of annotated data. This study explores the use of synthetic images for data augmentation to address the challenge of limited annotated data in colonoscopy lesion classifica...
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