AIMC Topic: Colonoscopy

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Deep reconstruction-recoding network for unsupervised domain adaptation and multi-center generalization in colonoscopy polyp detection.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Currently, the best performing methods in colonoscopy polyp detection are primarily based on deep neural networks (DNNs), which are usually trained on large amounts of labeled data. However, different hospitals use different...

Upper endoscopy photodocumentation quality evaluation with novel deep learning system.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: Visualization and photodocumentation during endoscopy procedures are suggested to be one indicator for endoscopy performance quality. However, this indicator is difficult to measure and audit manually in clinical practice. Artificial inte...

Detecting colon polyps in endoscopic images using artificial intelligence constructed with automated collection of annotated images from an endoscopy reporting system.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
BACKGROUND: Artificial intelligence (AI) has made considerable progress in image recognition, especially in the analysis of endoscopic images. The availability of large-scale annotated datasets has contributed to the recent progress in this field. Da...

Deep neural network for video colonoscopy of ulcerative colitis: a cross-sectional study.

The lancet. Gastroenterology & hepatology
BACKGROUND: A combination of endoscopic and histological evaluation is important in the management of patients with ulcerative colitis. We aimed to adapt our previous deep neural network system (deep neural ulcerative colitis [DNUC]) to full video co...

Mutual-Prototype Adaptation for Cross-Domain Polyp Segmentation.

IEEE journal of biomedical and health informatics
Accurate segmentation of the polyps from colonoscopy images provides useful information for the diagnosis and treatment of colorectal cancer. Despite deep learning methods advance automatic polyp segmentation, their performance often degrades when ap...

A Deep Learning Approach for Colonoscopy Pathology WSI Analysis: Accurate Segmentation and Classification.

IEEE journal of biomedical and health informatics
Colorectal cancer (CRC) is one of the most life-threatening malignancies. Colonoscopy pathology examination can identify cells of early-stage colon tumors in small tissue image slices. But, such examination is time-consuming and exhausting on high re...

Multi-step validation of a deep learning-based system for the quantification of bowel preparation: a prospective, observational study.

The Lancet. Digital health
BACKGROUND: Inadequate bowel preparation is associated with a decrease in adenoma detection rate (ADR). A deep learning-based bowel preparation assessment system based on the Boston bowel preparation scale (BBPS) has been previously established to ca...