Colorectal Polyps Characterization
6 papers with code • 4 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Colorectal Polyps Characterization
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Datasets
Kvasir-SEG
Kvasir
Kvasir-Sessile dataset
UNITOPATHO
CRC
KvasirCapsule-SEG
Kvasir-Capsule
Most implemented papers
ResUNet++: 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.
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
DebeshJha/2020-CBMS-DoubleU-Net
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The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks.
DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
Colonoscopy is the gold standard for examination and detection of colorectal polyps.
UniToPatho, 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.
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices.
