AIMC Topic: Colonic Polyps

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PFPRNet: A Phase-Wise Feature Pyramid With Retention Network for Polyp Segmentation.

IEEE journal of biomedical and health informatics Feb 10, 2025
Early detection of colonic polyps is crucial for the prevention and diagnosis of colorectal cancer. Currently, deep learning-based polyp segmentation methods have become mainstream and achieved remarkable results. Acquiring a large number of labeled ...
Algorithms Databases, Factual Deep Learning Humans Colonic Polyps Image Interpretation, Computer-Assisted
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Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systems.

Endoscopy Jan 30, 2025
BACKGROUND:  Artificial intelligence (AI) systems in endoscopy are predominantly developed and tested using high-quality imagery from expert centers. However, their performance may be different when applied in clinical practice, partly due to the div...
Artificial Intelligence Sensitivity and Specificity Colorectal Neoplasms Humans Colonic Polyps Colonoscopy Barrett Esophagus Diagnosis, Computer-Assisted Image Enhancement
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Effect of artificial intelligence-aided differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists' therapy management.

European radiology Jan 25, 2025
OBJECTIVES: Adenomatous colorectal polyps require endoscopic resection, as opposed to non-adenomatous hyperplastic colorectal polyps. This study aims to evaluate the effect of artificial intelligence (AI)-assisted differentiation of adenomatous and n...
Colorectal Neoplasms Male Middle Aged Adult Humans Sensitivity and Specificity Artificial Intelligence Colonic Polyps Retrospective Studies Adenomatous Polyps Diagnosis, Differential Colonography, Computed Tomographic Radiologists Radiographic Image Interpretation, Computer-Assisted Female Aged Aged, 80 and over
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Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach.

Scientific reports Jan 25, 2025
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...
Supervised Machine Learning Colonic Polyps Cluster Analysis Unsupervised Machine Learning Colorectal Neoplasms Humans Colonoscopy Neural Networks, Computer Support Vector Machine Algorithms
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Synthesized colonoscopy dataset from high-fidelity virtual colon with abnormal simulation.

Computers in biology and medicine Jan 17, 2025
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...
Humans Colonic Polyps Colonography, Computed Tomographic Colonoscopy Deep Learning Imaging, Three-Dimensional Databases, Factual Colon
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Dynamic spectrum-driven hierarchical learning network for polyp segmentation.

Medical image analysis Jan 14, 2025
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...
Machine Learning Algorithms Colonoscopy Colonic Polyps Image Interpretation, Computer-Assisted Humans
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FocusUNet: Pioneering dual attention with gated U-Net for colonoscopic polyp segmentation.

Computers in biology and medicine Jan 10, 2025
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...
Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Colonic Polyps Humans Neural Networks, Computer Colorectal Neoplasms Colonoscopy
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G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.

International journal of computer assisted radiology and surgery Jan 9, 2025
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...
Colonography, Computed Tomographic Reproducibility of Results Colon Deep Learning Imaging, Three-Dimensional Radiographic Image Interpretation, Computer-Assisted Colonic Polyps Humans Algorithms
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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation.

Computers in biology and medicine Dec 31, 2024
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...
Humans Colonic Polyps Neural Networks, Computer Image Interpretation, Computer-Assisted Algorithms Colorectal Neoplasms Image Processing, Computer-Assisted
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Current Status of Artificial Intelligence Use in Colonoscopy.

Digestion Dec 26, 2024
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...
Early Detection of Cancer Adenoma Colonic Polyps Humans Diagnosis, Computer-Assisted Deep Learning Artificial Intelligence Colorectal Neoplasms Colonoscopy
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