Abstract
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.
Abstract (translated)
心脏 Cine 磁共振成像(MRI)已经被用于 characterizing 心血管疾病(CVD),常常提供无侵入性phenotyping工具。~ 虽然目前使用 cine MRI 进行深度学习的方法取得了准确的特征characterization结果,但性能往往因为小的训练样本而受到影响。此外,许多深度学习模型被认为是“黑盒子”,模型在如何生成预测以及它们是否可靠方面仍然存在很大的不确定性。为了缓解这种情况,本工作提出了一种轻量级后继 subspace learning (SSL)框架,以CVD 分类为例,基于可解释的前向设计,并结合心脏模版。具体来说,我们的分层 SSL 模型基于(i) 相邻微点扩展,(ii) 未监督 subspace 近似,(iii) 监督回归,以及(iv) 多层次特征集成。此外,使用两个阶段的3D变形 fields,包括终末静息和终末收缩阶段,从模版和个人 subjects 输入提供客观的评估方法,即使训练样本很小。我们在 ACDC2017 数据库中评估了我们的框架,其中包括一个健康组和四个疾病组。与基于3D卷积神经网络的方法相比,我们的框架在参数数量上减少了140倍,表现出更好的分类性能,这支持它在临床实践中的潜在价值。
URL
https://arxiv.org/abs/2301.08959
