AIMC Topic: Polyps

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Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study.

Cancer medicine Mar 1, 2025
OBJECTIVE: Gallbladder polyps (GBPs) are increasingly prevalent, with the majority being benign; however, neoplastic polyps carry a risk of malignant transformation, highlighting the importance of accurate differentiation. This study aimed to develop...
Middle Aged ROC Curve Adult Male Cholecystectomy Polyps Gallbladder Diseases Gallbladder Neoplasms Algorithms Retrospective Studies Female Aged Humans Machine Learning
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Assessing the Impact of Federated Learning and Differential Privacy on Multi-centre Polyp Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Federated Learning (FL) is emerging in the medical field to address the need for diverse datasets while complying with data protection regulations. This decentralised learning paradigm allows hospitals (clients) to train machine learning models local...
Machine Learning Algorithms Humans Computer Security Polyps Image Processing, Computer-Assisted Privacy Federated Learning
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Enhanced segmentation of gastrointestinal polyps from capsule endoscopy images with artifacts using ensemble learning.

World journal of gastroenterology Nov 7, 2022
BACKGROUND: Endoscopy artifacts are widespread in real capsule endoscopy (CE) images but not in high-quality standard datasets.
Gastrointestinal Neoplasms Artifacts Capsule Endoscopy Polyps Humans Deep Learning
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APRNet: Alternative Prediction Refinement Network for Polyp Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Colorectal cancer has become the second leading cause of cancer-related death, attracting considerable interest for automatic polyp segmentation in polyp screening system. Accurate segmentation of polyps from colonoscopy is a challenging task as the ...
Humans Neural Networks, Computer Algorithms Colonoscopy Polyps Image Processing, Computer-Assisted
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EMS-Net: Enhanced Multi-Scale Network for Polyp Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
In recent years, polyp segmentation plays an important role in the diagnosis and treatment of colorectal cancer. Accurate segmentation of polyps is very challenging due to different sizes, shapes, and unclear boundaries. Making full use of multi-scal...
Polyps Neural Networks, Computer Colonoscopy Image Processing, Computer-Assisted Attention Humans
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A 3D Convolutional Neural Network Framework for Polyp Candidates Detection on the Limited Dataset of CT Colonography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Proper training of convolutional neural networks (CNNs) requires annotated training datasets oflarge size, which are not currently available in CT colonography (CTC). In this paper, we propose a well-designed framework to address the challenging prob...
Polyps Colonography, Computed Tomographic Humans Diagnosis, Computer-Assisted Neural Networks, Computer Colon
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Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Colorectal cancer is one of the highest causes of cancer-related death, especially in men. Polyps are one of the main causes of colorectal cancer, and early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps ...
Biological Phenomena Polyps Colonic Polyps Humans Neural Networks, Computer Colorectal Neoplasms Colonoscopy
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Deep learning for polyp recognition in wireless capsule endoscopy images.

Medical physics Apr 1, 2017
PURPOSE: Wireless capsule endoscopy (WCE) enables physicians to examine the digestive tract without any surgical operations, at the cost of a large volume of images to be analyzed. In the computer-aided diagnosis of WCE images, the main challenge ari...
Image Processing, Computer-Assisted Polyps Machine Learning Capsule Endoscopy
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