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Abstract #2988

Deep learning-based detection of liver disease using MRI

Mark A Pinnock1,2, Yipeng Hu1,2, Alan Bainbridge3, David Atkinson4, Rajeshwar P Mookerjee5, Stuart A Taylor4, Dean C Barratt1,2, and Manil D Chouhan4
1Centre for Medical Image Computing, University College London, London, United Kingdom, 2Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom, 3Department of Medical Physics and Biomedical Engineering, University College London Hospitals NHS Foundation Trust, London, United Kingdom, 4Centre for Medical Imaging, Division of Medicine, University College London, London, United Kingdom, 5Institute for Liver and Digestive Health, Division of Medicine, University College London, London, United Kingdom

Traditional approaches to MRI detection of liver disease require specialist hardware, sequences and post-processing. Here we propose a deep learning (DL) based model for the detection of liver disease using standard T2-weighted anatomical sequences, as an early feasibility study for the potential of DL-based classification of liver disease severity. Our DL model achieved a diagnostic accuracy of 0.92 on unseen data and achieved a test accuracy of 0.75 when trained with relevant anatomical segmentation masks without images, demonstrating potential scanner/sequence independence. Lastly, we used DL interpretability techniques to analyse failure cases.

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