This GitHub repository contains all the analysis code used in, "Characterization of single cell transcriptomic responses to osteoarthritis in a naturally occurring equine model."
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Updated
Nov 12, 2024 - R
This GitHub repository contains all the analysis code used in, "Characterization of single cell transcriptomic responses to osteoarthritis in a naturally occurring equine model."
Image analysis approaches to analyze the OAI magnetic resonance images
Hourglass Networks for Knee Anatomical Landmark Localization: PyTorch Implementation
Methylation and mqtl analysis of the developing knee
Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative
A collection of code and resources for creating primary databases, analyzing rheological data for Osteoarthritis research, and generating technical reports for the results.
Codes for paper: Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach
Cartilage analysis for Toluidine blue slides
AI is still seen as a magic black box, it is non-intuitive, and difficult for people to understand and trust. In the medical image analysis field, this problem is amplified and leads to a limited adoption of such AI systems. The proposed projects aim to adapt the exciting explainability tools for the medical domain to demystify and better explai…
Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data
Knee MRI cartilage segmentation model used in 2019 IWOAI segmentation challenge
An international study of surgery for wrist arthritis and its outcomes, initiated by a Studyathon run by the British Society of Surgery of the Hand, 8th Sept 2021
Matlab code for calculating and displaying MRI T1rho images
Deep-Learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed Tomography
Codes for paper: A novel method for automatic localization of joint area on knee plain radiographs by A. Tiulpin et. al.
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