COVID deterioration prediction based on chest X-ray radiographs via MoCo-trained image representations

Logiciel de détection, localisation & segmentation de carries sur des radiographies dentaires.

Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative

A contrast enhancement approach involving non linear mapping of Laplacian pyramid.

ROVER: an open source hybrid-parallel library for volume rendering and simulated radiography

Autoscoper is a 2D-3D image registration software package.

OpenSource project collaborating with a healthcare mutual insurance company (data provider) focusing on classifying knee frontal radiographies (DICOM) according to Schatzker classification (0 to 6).

This is a set of functions for generating digitally reconstructed radiographs from CT data

Python package for a systems approach to blur estimation and reduction

Building an AI model for chest X-ray under patient privacy guarantees

Developed a Convolutional Neural Network based on VGG16 architecture to diagnose COVID-19 and classify chest X-rays of patients suffering from COVID-19, Ground Glass Opacity and Viral Pneumonia. This repository contains the link to the dataset, python code for visualizing the obtained data and developing the model using Keras API.

Demo scripts for the python package pysaber

Multiple use case distributed memory image compositing

X-ray Film Digitalization

Research in computational medical physics and deep learning. Conducted at Perelman School of Medicine, UPenn, between April 2020 and December 2020.

Scan With Me (SWiM)

Interactive demonstrations of medical physics imaging applications for Spring 2024 BIOS29326

2024-1 XAI611 고급빅데이터분석 Project Proposal

JointNET is a deep learning model designed to predict active inflammation in sacroiliac joints using radiographs. Developed using a dataset of 1,537 grade 0 SIJs, the model showcases superior accuracy compared to human observers. This repository contains the code used in the development and validation of JointNET.