This repo is used for MediaEval2020 Workshop (Medio Track)
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Updated
Dec 29, 2020 - Python
This repo is used for MediaEval2020 Workshop (Medio Track)
A simple research on Polyp-Segmentation
Implemented Unet++ models for medical image segmentation to detect and classify colorectal polyps.
The official repository for MedAI 2021 - a machine learning challenge organized by NORA and NMI
Codes for MICCAI2021 paper "Shallow Attention Network for Polyp Segmentation"
Baseline model for BKAI-IGH_Neopolyp. Currently supports Unet and Attention Unet with VGG-16, MobilenetV2 and Efficientnet-B0 backbone. This repository is private therefore not a official implementation from BKAI.
Polyp Segmentation and Phase Classification from Endoscopic Images
PolypSeg+: a Lightweight Context-aware Network for Real-time Polyp Segmentation
TGANet: Text-guided attention for improved polyp segmentation [Early Accepted & Student Travel Award at MICCAI 2022]
Performed Medical Image Segmentation Using (UNet, DoubleUnet and ResUnetplusplus)
Official PyTorch implementation of UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation (ACMMM 2021)
TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation
This projects uses video feeds from endoscopic procedures to identify polyps in the gastrointestinal tract and draw masks around them to aid doctors in identifying precursors of colorectal cancer.
PraNet: Parallel Reverse Attention Network for Polyp Segmentation, MICCAI 2020 (Oral). Code using Jittor Framework is available.
Polyp-SAM++ is the first text-guided polyp-segmentation method using segment anything model (SAM).
Epistemic uncertainty, sometimes referred to as model uncertainty, describes what the model does not know because training data was not appropriate. Modelling epistemic uncertainty is crucial to prevent ill advised discussion making due to over confident models.
Kvasir-SEG: A Segmented Polyp Dataset
Official implementation of NanoNet: Real-time medical Image segmentation architecture (IEEE CBMS)
A multi-centre polyp detection and segmentation dataset for generalisability assessment https://www.nature.com/articles/s41597-023-01981-y
Official implementation of DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation (pytorch implementation)
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