Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
This work addresses the need for interpretable deep learning tools in medical imaging to improve diagnostic accuracy and risk-stratification for clinicians, though it is incremental in applying existing generative methods to a specific domain.
The authors tackled the problem of diagnosing cardiac diseases by developing a 3D convolutional generative model that learns interpretable anatomical features from medical images, achieving 100% accuracy on their dataset and 90% on an external benchmark for classifying healthy and hypertrophic cardiomyopathy subjects.
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.