将脑白质纤维的各向异性值转为图像，提升深度学习可解释性。
An Interpretable Representation Learning Approach for Diffusion Tensor Imaging
- 用9×9灰度图表示纤维的各向异性值，构建新2D表征
- 在性别分类任务中F1得分比基线高15.74%
- 模型生成的潜在表示更解耦、可解释，适合神经科学研究
扩散张量成像（DTI）轨迹追踪可提供大脑结构连接的详细信息，但在深度学习模型中存在表示与解释困难。本文提出一种新型2D DTI轨迹表征方法，将纤维层面的各向异性（FA）值编码为9×9灰度图像，并通过带空间广播解码器的Beta-总相关变分自编码器学习解耦且可解释的潜在嵌入。我们采用监督与无监督表示学习策略评估嵌入质量，包括辅助分类、三元组损失和基于SimCLR的对比学习。相比1D组深度神经网络基线，本方法在下游性别分类任务中F1分数提升15.74%，且优于3D表征的解耦能力。
原文摘要 · Abstract (English)
Diffusion Tensor Imaging (DTI) tractography offers detailed insights into the structural connectivity of the brain, but presents challenges in effective representation and interpretation in deep learning models. In this work, we propose a novel 2D representation of DTI tractography that encodes tract-level fractional anisotropy (FA) values into a 9x9 grayscale image. This representation is processed through a Beta-Total Correlation Variational Autoencoder with a Spatial Broadcast Decoder to learn a disentangled and interpretable latent embedding. We evaluate the quality of this embedding using supervised and unsupervised representation learning strategies, including auxiliary classification, triplet loss, and SimCLR-based contrastive learning. Compared to the 1D Group deep neural network (DNN) baselines, our approach improves the F1 score in a downstream sex classification task by 15.74% and shows a better disentanglement than the 3D representation.
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