polyp-detection

Implementing polyp segmentation using the U-Net and CVC-612 dataset.

Official implementation of ColonSegNet: Real-Time Polyp Segmentation (Used in NVIDIA Clara Holoscan App for Polyp Segmentation)

A multi-centre polyp detection and segmentation dataset for generalisability assessment https://www.nature.com/articles/s41597-023-01981-y

Kvasir-SEG: A Segmented Polyp Dataset

[MICCAI 2025 Early Accept] Targeted False Positive Synthesis via Detector-guided Adversarial Diffusion Attacker for Robust Polyp Detection

Polyp Localization In Colonscopy Videos using Single Shot Multibox Detector

Official repo of "EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)"

This repository is a collection of Python scripts and Jupyter notebooks for understanding the performance improvement in image classification, object detection and instance segmentation with OpenVINO. It also contains reference implementations of dwell time analytics, ALPR and polyp detection.

A systematic study on the performance of different data augmentation methods for colon polyp detection.

Polyp-Classification-using-CNN

AI-powered colonoscopy polyp segmentation using Attention U-Net. Full-stack web app with Next.js frontend and Flask backend, achieving 76.81% Dice score on CVC-ClinicDB dataset.

This project benchmarks modern object detection models (YOLOv8–v12, YOLO26, YOLOE, YOLO-World, RT-DETR) for automatic polyp detection.

Real-time polyp detection and tracking in colonoscopy images using YOLOv8m-seg — instance segmentation with ByteTrack for cross-dataset generalization.

Towards One-stage Framework: Optimization of 3D FCNs for Polyp Detection in CT Colonography

A curated collection of gastrointestinal endoscopy datasets for AI research, covering colonoscopy, polyp detection, segmentation, classification, capsule endoscopy, surgical endoscopy, medical VQA, and multimodal learning.

This project aims to explore Domain Generalisation for Cross-Institutional Medical Image Analysis using PolypGen Dataset

Semantic image segmentation for automatic polyp detection using U-Net and Kvasir-SEG.

This repository contains the BKM for training YOLOv11n model on Intel Arc A770 GPU and the reference implementation of polyp detection in colonoscopy video with the optimized model using OpenVINO 2025

A temporal computer vision system for detecting missed polyps in colonoscopies using a Dual-Path architecture (Spatial RetinaNet + Temporal ConvGRU) and a clinical fusion engine to reduce false positives.

This research will show an innovative method useful in the segmentation of polyps during the screening phases of colonoscopies. To do this we have adopted a new approach which consists in merging the hybrid semantic network (HSNet) architecture model with the Reagion-wise(RW) as a loss function for the backpropagation process.