  Cardiac cine magnetic resonance imaging (MRI) has been used to characterize
cardiovascular diseases (CVD), often providing a noninvasive phenotyping
tool.~While recently flourished deep learning based approaches using cine MRI
yield accurate characterization results, the performance is often degraded by
small training samples. In addition, many deep learning models are deemed a
``black box," for which models remain largely elusive in how models yield a
prediction and how reliable they are. To alleviate this, this work proposes a
lightweight successive subspace learning (SSL) framework for CVD
classification, based on an interpretable feedforward design, in conjunction
with a cardiac atlas. Specifically, our hierarchical SSL model is based on (i)
neighborhood voxel expansion, (ii) unsupervised subspace approximation, (iii)
supervised regression, and (iv) multi-level feature integration. In addition,
using two-phase 3D deformation fields, including end-diastolic and end-systolic
phases, derived between the atlas and individual subjects as input offers
objective means of assessing CVD, even with small training samples. We evaluate
our framework on the ACDC2017 database, comprising one healthy group and four
disease groups. Compared with 3D CNN-based approaches, our framework achieves
superior classification performance with 140$\times$ fewer parameters, which
supports its potential value in clinical use.
