Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning.
Sebastian NowakNarine MesropyanAnton FaronWolfgang BlockMartin ReuterUlrike I AttenbergerJulian A LuetkensAlois M SprinkartPublished in: European radiology (2021)
• A pipeline consisting of two convolutional neural networks (CNNs) pre-trained on an extensive natural image database (ImageNet archive) enables detection of liver cirrhosis on standard T2-weighted MRI. • High classification accuracy can be achieved even without altering the pre-trained parameters of the convolutional neural networks. • Other abdominal structures apart from the liver were relevant for detection when the network was trained on unsegmented images.
Keyphrases
- convolutional neural network
- deep learning
- contrast enhanced
- loop mediated isothermal amplification
- resistance training
- magnetic resonance imaging
- magnetic resonance
- label free
- machine learning
- real time pcr
- network analysis
- computed tomography
- emergency department
- mass spectrometry
- high resolution
- body composition
- diffusion weighted imaging
- high intensity
- sensitive detection