Colorectal Polyps Characterization

6 papers with code • 4 benchmarks • 8 datasets

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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


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.