AIMC Topic: Colonic Polyps

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Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach.

Scientific reports
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and a...

Synthesized colonoscopy dataset from high-fidelity virtual colon with abnormal simulation.

Computers in biology and medicine
With the advent of the deep learning-based colonoscopy system, the need for a vast amount of high-quality colonoscopy image datasets for training is crucial. However, the generalization ability of deep learning models is challenged by the limited ava...

Dynamic spectrum-driven hierarchical learning network for polyp segmentation.

Medical image analysis
Accurate automatic polyp segmentation in colonoscopy is crucial for the prompt prevention of colorectal cancer. However, the heterogeneous nature of polyps and differences in lighting and visibility conditions present significant challenges in achiev...

FocusUNet: Pioneering dual attention with gated U-Net for colonoscopic polyp segmentation.

Computers in biology and medicine
The detection and excision of colorectal polyps, precursors to colorectal cancer (CRC), can improve survival rates by up to 90%. Automated polyp segmentation in colonoscopy images expedites diagnosis and aids in the precise identification of adenomat...

G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.

International journal of computer assisted radiology and surgery
PURPOSE: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settin...

MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation.

Computers in biology and medicine
Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of dat...

Current Status of Artificial Intelligence Use in Colonoscopy.

Digestion
BACKGROUND: Artificial intelligence (AI) has significantly impacted medical imaging, particularly in gastrointestinal endoscopy. Computer-aided detection and diagnosis systems (CADe and CADx) are thought to enhance the quality of colonoscopy procedur...

A clinical pilot trial of an artificial intelligence-driven smart phone application of bowel preparation for colonoscopy: a randomized clinical trial.

Scandinavian journal of gastroenterology
BACKGROUND: High-quality bowel preparation is paramount for a successful colonoscopy. This study aimed to explore the effect of artificial intelligence-driven smartphone software on the quality of bowel preparation.

CE-Net: Cascade attention and context-aware cross-level fusion network via edge learning guidance for polyp segmentation.

Computers in biology and medicine
Colorectal polyps are one of the most direct causes of colorectal cancer. Polypectomy can effectively block the process of colorectal cancer, but accurate polyp segmentation methods are required as an auxiliary means. However, there are several chall...