Papers Colorectal Polyps Characterization
“Colorectal Polyps Characterization” 태그가 달린 논문 8편 · 필터 해제
Feature Selection Gates with Gradient Routing for Endoscopic Image Computing
To address overfitting and enhance model generalization in gastroenterological polyp size assessment, our study introduces Feature Selection Gates (FSG) alongside Gradient Routing (GR) for dynamic feature selection. This…
Binary ClassificationColorectal Polyps Characterizationfeature selectionCAD systems for colorectal cancer from WSI are still not ready for clinical acceptance
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples ar…
Colorectal Polyps Characterizationwhole slide imagesNanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time …
Colorectal Polyps CharacterizationInstrument RecognitionMedical Image SegmentationReal-Time Semantic Segmentation+3UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading
Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterizatio…
Colorectal Polyps CharacterizationGeneral ClassificationHistopathological Image ClassificationManagement+1DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
Colonoscopy is the gold standard for examination and detection of colorectal polyps. Localization and delineation of polyps can play a vital role in treatment (e.g., surgical planning) and prognostic decision making. Pol…
Colorectal Polyps CharacterizationDecision MakingDecoderMedical Image Segmentation+2Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of…
BenchmarkingColorectal Polyps CharacterizationMedical Image SegmentationMedical Object Detection+4DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image …
Cell SegmentationColorectal Polyps CharacterizationImage SegmentationLesion Segmentation+5ResUNet++: An Advanced Architecture for Medical Image Segmentation
Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully…
Colorectal Polyps CharacterizationImage SegmentationMedical Image SegmentationPolyp Segmentation+2