https://github.com/jraad/unsupervised_neonatal_anomaly_detection

https://github.com/jraad/unsupervised_neonatal_anomaly_detection

Last synced: 2 months ago
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• Host: GitHub
• URL: https://github.com/jraad/unsupervised_neonatal_anomaly_detection
• Owner: jraad
• License: apache-2.0
• Created: 2023-04-08T16:31:23.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-04-08T17:27:34.000Z (about 2 years ago)
• Last Synced: 2024-07-31T20:42:17.525Z (10 months ago)
• Language: Jupyter Notebook
• Size: 12.4 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Unsupervised-Anomaly-Detection-in-Brain-MRI - [Code

README

        
### Unsupervised Anomaly Detection in Neonates

This repository contains code associated with out publication entitled "Unsupervised Abnormality Detection in Neonatal MRI Brain Scans Using Deep Learning" - Jad Dino Raad, Ratna Babu Chinnam, Suzan Arslanturk, Sidhartha Tan, Jeong-Won Jeong, and Swati Mody (currently under review at Scientific Reports).

In this research, we explore 3D Autoencoder (AE) and Variational Autoencoder (VAE) architectures for identifying anomalies in neonatal MRI brain scans. We train our architectures on normal T2-weighted brain volumes from the Developing Human Connectome Project, and test our approaches on abnormal data from the same dataset.