polyp

2021-MICCAI-Progressively Normalized Self-Attention Network for Video Polyp Segmentation

Frontiers in Intelligent Colonoscopy [ColonSurvey | ColonINST | ColonGPT]

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

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

Project COLON-X (Shaping the neXt frontier in intelligent COLONoscopy)

Kvasir-SEG: A Segmented Polyp Dataset

[TMI'20] Learn to Threshold: ThresholdNet with Confidence-Guided Manifold Mixup for Polyp Segmentation

Code Implementation of the FCB-SwinV2 Transformer

Polyp Classification and Clustering from Endoscopic Images using Competitive and Convolutional Neural Networks

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

TEMory, a model featuring a Temporal Encoder and Memory Unit, designed for weakly supervised colonic polyp frame detection with a comprehensive understanding of normal frame characteristics.

Internship Project N°1_ HealthDatascience_HealthcareEngineering

Reactive atypia in an ulcerated colon polyp - Ülsere Kolon Polibinde Reaktif Atipi