Polyp Localization In Colonscopy Videos using Single Shot Multibox Detector

Polyp-Classification-using-CNN

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

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

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

[MICCAI'22] Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection.

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

[TMI'22] A Source-free Domain Adaptive Polyp Detection Framework with Style Diversification Flow

Kvasir-SEG: A Segmented Polyp Dataset

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

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.

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

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

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

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.