AIMC Topic: Colonic Polyps

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CFM-UNet: coupling local and global feature extraction networks for medical image segmentation.

Scientific reports Jul 1, 2025
In medical image segmentation, traditional CNN-based models excel at extracting local features but have limitations in capturing global features. Conversely, Mamba, a novel network framework, effectively captures long-range feature dependencies and e...
Liver Neoplasms Image Processing, Computer-Assisted Colonic Polyps Liver Humans Neural Networks, Computer Algorithms
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A novel approach to overcome black box of AI for optical diagnosis in colonoscopy.

Scientific reports Jul 1, 2025
Accurate real-time optical diagnosis that distinguishes neoplastic from non-neoplastic colorectal lesions during colonoscopy can lower the costs of pathological assessments, prevent unnecessary polypectomies, and help avoid adverse events. Using a mu...
Sensitivity and Specificity Colorectal Neoplasms Male Artificial Intelligence Female Colonic Polyps Adenomatous Polyps Narrow Band Imaging Colonoscopy Deep Learning Humans
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Taking the Guess Work Out of Endoscopic Polyp Measurement: From Traditional Methods to AI.

Journal of clinical gastroenterology Jul 1, 2025
Colonoscopy is a crucial tool for evaluating lower gastrointestinal disease, monitoring high-risk patients for colorectal neoplasia, and screening for colorectal cancer. In the United States, over 14 million colonoscopies are performed annually, with...
Colorectal Neoplasms Colonoscopy Early Detection of Cancer Humans Artificial Intelligence Colonic Polyps
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Deep learning model applied to real-time delineation of colorectal polyps.

BMC medical informatics and decision making Jun 4, 2025
BACKGROUND: Deep learning models have shown considerable potential to improve diagnostic accuracy across medical fields. Although YOLACT has demonstrated real-time detection and segmentation in non-medical datasets, its application in medical setting...
Deep Learning Colonic Polyps Image Interpretation, Computer-Assisted Humans Colonoscopy
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DCATNet: polyp segmentation with deformable convolution and contextual-aware attention network.

BMC medical imaging Apr 14, 2025
Polyp segmentation is crucial in computer-aided diagnosis but remains challenging due to the complexity of medical images and anatomical variations. Current state-of-the-art methods struggle with accurate polyp segmentation due to the variability in ...
Image Interpretation, Computer-Assisted Humans Deep Learning Colonic Polyps Algorithms
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EFCNet enhances the efficiency of segmenting clinically significant small medical objects.

Scientific reports Apr 14, 2025
Efficient segmentation of small hyperreflective dots, key biomarkers for diseases like macular edema, is critical for diagnosis and treatment monitoring.However, existing models, including Convolutional Neural Networks (CNNs) and Transformers, strugg...
Colonoscopy Image Processing, Computer-Assisted Macular Edema Neural Networks, Computer Colonic Polyps Algorithms Tomography, Optical Coherence Humans
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Enhancing polyp classification: A comparative analysis of spatio-temporal techniques.

Medical engineering & physics Apr 7, 2025
Colorectal cancer (CRC) is a major health concern, ranking as the third deadliest cancer globally. Early diagnosis of adenomatous polyps which are pre-cancerous abnormal tissue growth, is crucial for preventing CRC. Artificial intelligence-assisted n...
Colonic Polyps Humans Spatio-Temporal Analysis Colonoscopy Image Processing, Computer-Assisted
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Application of an Automated Deep Learning Program to A Diagnostic Classification Model: Differentiating High-Risk Adenomas Among Colorectal Polyps 10 mm or Smaller.

Journal of digestive diseases Apr 2, 2025
OBJECTIVE: This study aimed to develop a computer-aided diagnosis (CADx) model using an automated deep learning (DL) program to classify low- and high-risk adenomas among colorectal polyps ≤ 10 mm with standard white-light endoscopy.
Adenoma Humans Diagnosis, Differential Deep Learning Colonoscopy Colonic Polyps Colorectal Neoplasms ROC Curve Diagnosis, Computer-Assisted
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A Novel Natural Language Processing Tool Improves Colonoscopy Auditing of Adenoma and Serrated Polyp Detection Rates.

Journal of gastroenterology and hepatology Mar 31, 2025
BACKGROUND AND STUDY AIMS: Determining adenoma detection rate (ADR) and serrated polyp detection rate (SDR) can be challenging as they usually involve manual matching of colonoscopy and histology reports. This study aimed to validate a Natural Langua...
Humans Male Colonoscopy Female Medical Audit Colonic Neoplasms Adenoma Colonic Polyps Natural Language Processing Quality Indicators, Health Care
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Towards full integration of explainable artificial intelligence in colon capsule endoscopy's pathway.

Scientific reports Feb 18, 2025
Despite recent surge of interest in deploying colon capsule endoscopy (CCE) for early diagnosis of colorectal diseases, there remains a large gap between the current state of CCE in clinical practice, and the state of its counterpart optical colonosc...
Capsule Endoscopy Image Processing, Computer-Assisted Colonic Polyps Colon Colonoscopy Artificial Intelligence Algorithms Humans Colorectal Neoplasms Neural Networks, Computer
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