AIMC Topic: Colonic Polyps

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Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain.

IEEE journal of biomedical and health informatics Dec 5, 2016
Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Although polypectomy at early stage reduces CRC incidence, 90% of the polyps are small and diminutive, where removal of them poses risks to patients that may outweigh the benefits...
Humans Neural Networks, Computer Colonic Polyps Image Interpretation, Computer-Assisted Machine Learning Colonoscopy ROC Curve
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Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification.

Computational and mathematical methods in medicine Oct 26, 2016
Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features ...
Colonoscopy Diagnosis, Computer-Assisted Image Processing, Computer-Assisted Colonic Polyps Video Recording Pattern Recognition, Automated Algorithms Reproducibility of Results Endoscopy Neural Networks, Computer Machine Learning Software Humans
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Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

IEEE transactions on medical imaging Mar 7, 2016
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that ha...
Colonoscopy ROC Curve Diagnostic Imaging Humans Neural Networks, Computer Machine Learning Computed Tomography Angiography Pulmonary Embolism Colonic Polyps Image Interpretation, Computer-Assisted
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Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation.

IEEE transactions on medical imaging Sep 28, 2015
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...
Humans Neural Networks, Computer Adolescent Machine Learning Child Databases, Factual Lymph Nodes Tomography, X-Ray Computed Adult Spinal Neoplasms Female Radiographic Image Interpretation, Computer-Assisted Male Aged Colonic Polyps Middle Aged Young Adult
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Colonoscopy with robotic steering and automated lumen centralization: a feasibility study in a colon model.

Endoscopy Jun 30, 2015
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.
Models, Anatomic Male Feasibility Studies Pilot Projects Middle Aged Cross-Over Studies Humans Colonic Polyps Clinical Competence Colonoscopy Female Robotics Adult
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Polyp Detection via Imbalanced Learning and Discriminative Feature Learning.

IEEE transactions on medical imaging May 18, 2015
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...
Least-Squares Analysis Colonic Polyps Image Interpretation, Computer-Assisted Algorithms Colon Colonoscopy Humans Machine Learning
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Multi-center colonoscopy quality measurement utilizing natural language processing.

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...
Colonic Polyps Early Detection of Cancer Reference Standards Humans Medical Records Colorectal Neoplasms Adenoma Hyperplasia Colonoscopy Natural Language Processing
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Efficient polyp detection algorithm based on deep learning.

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...
Humans Detection Algorithms Algorithms Deep Learning Colorectal Neoplasms Colonic Polyps Colonoscopy Image Interpretation, Computer-Assisted
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Boosting polyp screening with improved point-teacher weakly semi-supervised.

Computers in biology and medicine Jun 1, 2025
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...
Neural Networks, Computer Humans Colonic Polyps
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Use of computer-assisted detection (CADe) colonoscopy in colorectal cancer screening and surveillance: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement.

Endoscopy Jun 1, 2025
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...
Humans Artificial Intelligence Early Detection of Cancer Europe Colorectal Neoplasms Colonic Polyps Societies, Medical Colonoscopy Diagnosis, Computer-Assisted
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