Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
By C. Biffi, Ozan Oktay, Giacomo Tarroni, Wenjia Bai, A. Marvao, Georgia Doumou, Martin Rajchl, R. Bedair, S. Prasad, S. Cook and 2 more. 2018
77 citations recorded by the source corpus.
Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images. Both factors limit the sensitivity in quantifying complex structural and functional phenotypes. Deep learning approaches have recently achieved success for tasks such as classification or segmentation of medical images, but lack interpretability in the feature extraction and decision processes, limiting their value in clinical diagnosis. In this work, we propose a 3D convolutional generative model for automatic classification of images from patients with cardiac diseases associated with structural remodeling. The model leverages interpretable task-specific anatomic patterns learned from 3D segmentations. It further allows to visualise and quantify the learned pathology-specific remodeling patterns in the original input space of the images. This approach yields high accuracy in the categorization of healthy and hypertrophic cardiomyopathy subjects when tested on unseen MR images from our own multi-centre dataset (100%) as well on the ACDC MICCAI 2017 dataset (90%). We believe that the proposed deep learning approach is a promising step towards the development of interpretable classifiers for the medical imaging domain, which may help clinicians to improve diagnostic accuracy and enhance patient risk-stratification.
Subfields: Cardiology and Cardiovascular Medicine, Generative Models, Medical Imaging
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Citations in the corpus
- Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “This generative framework has been adopted by several recent studies [366,367] mainly as an attribution method to relate a particular output of a Deep Learning model to their input variables.”
- A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, methodology): “[71] introduces activation atlases, which is similarly using t-SNE to arrange some activations {𝑓 𝑎𝑐𝑡 (𝑥)} , except that each point is represented by the average activations of feature visualization.”
- AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, unknown): “Specifically, for medical imaging, techniques described in [140] interpret predictions in a visually and semantically meaningful way while task-specific features in [141] are developed such that their deep learning system can make transparent classi-fication predictions.”
- Deep Learning for Cardiac Image Segmentation: A Review (2019, methodology): “In addition, these segmentation maps are essential for 3D shape analysis (60, 86), 3D + time motion analysis (87), and survival prediction (88).”
- Causability and explainability of artificial intelligence in medicine (2019, background): “Biffi et al. (2018) learn interpretable features for variational auto encoders (VAE) by learning gradients in the latent embedding space that it linked to the classification result.”