Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

Carlo Biffi; Ozan Oktay; Giacomo Tarroni; Wenjia Bai; Antonio de Marvao; Georgia Doumou; Martin Rajchl; Reem Bedair; Sanjay Prasad; Stuart A. Cook; Declan P. O’Regan; Daniel Rueckert

doi:10.1007/978-3-030-00934-2_52

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Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

Carlo Biffi Ozan Oktay Giacomo Tarroni Wenjia Bai Antonio de Marvao Georgia Doumou Martin Rajchl Reem Bedair Sanjay Prasad Stuart A. Cook Declan P. O’Regan Daniel Rueckert

Year: 2018 Lecture notes in computer science Pages: 464-471 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-00934-2_52

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Keywords:

Interpretability Artificial intelligence Computer science Deep learning Segmentation Convolutional neural network Categorization Machine learning Medical imaging Pattern recognition (psychology) Cardiac imaging Medicine Radiology

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Cardiovascular Function and Risk Factors

Health Sciences → Medicine → Cardiology and Cardiovascular Medicine

Cardiomyopathy and Myosin Studies

Health Sciences → Medicine → Cardiology and Cardiovascular Medicine

Cardiac Valve Diseases and Treatments

Health Sciences → Medicine → Cardiology and Cardiovascular Medicine

Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

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