Colorectal Polyps Characterization

6 papers with code • 4 benchmarks • 7 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

ResUNet++: An Advanced Architecture for Medical Image Segmentation

DebeshJha/ResUNetplusplus 16 Nov 2019

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 8 Jun 2020

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

DebeshJha/ColonSegNet 15 Nov 2020

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

nikhilroxtomar/DDANet 30 Dec 2020

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

EIDOSlab/UNITOPATHO 25 Jan 2021

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

DebeshJha/NanoNet 22 Apr 2021

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.