Laparoscopic video dataset for surgical action triplet recognition

This repo contains an implementation code for the weakly supervised surgical tool tracker. In this research, the temporal dependency in surgical video data is modeled using a convolutional LSTM which is trained only on image level labels to detect, localize and track surgical instruments.

Codes to process and train LIMUC dataset

[Dataset] A curated collection of endoscopic surgical datasets for advancing 3D reconstruction, segmentation, and motion estimation research in medical environments.

EndoCV2020: Endoscopic Artefact Detection Challenge (EAD2020) - Implementation of the proposed framework

GitHub repository for Medico automatic polyp segmentation challenge

Fusion at the Foregut: CLIP-Based Prototypical Learning with DINOv2 Refinement for Endoscopic Image Analysis

[MIA'21] Consolidated domain adaptive detection and localization framework for cross-device colonoscopic images

c++ and python visualize & streaming interface for super cheap endoscope cameras

video capsule endoscopy abnormality classification for capsule vision 2024 challenge

A patch-based Gastroscopic Classifier web app with Python backend using Flask micro-framework and PyTorch modified Resnet-34 Convolutional Neural Network.

Enhanced Neoplasia Detection in Endoscopic Images with Domain-Specific Pretraining and Focal Loss for Severe Class Imbalance

GitHub for Endotect 2020 Challenge

MATLAB project for identifying gastrointestinal abnormalities in endoscopic images using CNNs

This repository hosts the script that was utilized for report the results of the conference article: “Assessing deep learning methods for the identification of kidney stones in endoscopic images”

Proposed a novel method for image quality enhancement using diffusion models

A high-performance, asynchronous, zero-allocation MJPEG streaming server designed specifically for Useeplus USB borescope cameras on the Raspberry Pi 5.

This repository hosts the script that was utilized for report the results of the conference article: “Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning”

Implementation of Deep Learning Techniques in endoscopic images using Deep Learning