用已有影像生成缺失模态数据，助力阿尔茨海默病早期诊断。
Modality-Agnostic Style Transfer for Holistic Feature Imputation
- 通过对抗训练保留疾病特征，仅转移模态风格。
- 生成数据与真实数据差异极小（平均Cohen's d < 0.19）。
- 适合需补全多模态影像的临床研究者使用。
通过单一影像表征阿尔茨海默病（AD）前期阶段十分困难，因其早期症状隐匿。因此，许多神经影像研究整合了多种模态（如MRI和PET），但受制于采集条件，常出现数据缺失。本文提出一种框架，利用已有影像为特定受试者生成缺失的影像模态，减少额外检查需求。该框架在保持AD特异性内容的同时，迁移模态专属风格，通过域对抗训练保留模态无关但疾病相关的特征，并由生成对抗网络添加难以区分的模态风格。在阿尔茨海默病神经影像计划（ADNI）数据集上评估，生成数据质量优于其他插补方法。生成数据与真实数据间平均Cohen's d值小于0.19，表明合成数据在各类模态下均具备实际可用性。
原文摘要 · Abstract (English)
Characterizing a preclinical stage of Alzheimer's Disease (AD) via single imaging is difficult as its early symptoms are quite subtle. Therefore, many neuroimaging studies are curated with various imaging modalities, e.g., MRI and PET, however, it is often challenging to acquire all of them from all subjects and missing data become inevitable. In this regards, in this paper, we propose a framework that generates unobserved imaging measures for specific subjects using their existing measures, thereby reducing the need for additional examinations. Our framework transfers modality-specific style while preserving AD-specific content. This is done by domain adversarial training that preserves modality-agnostic but AD-specific information, while a generative adversarial network adds an indistinguishable modality-specific style. Our proposed framework is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and compared with other imputation methods in terms of generated data quality. Small average Cohen's $d$ $< 0.19$ between our generated measures and real ones suggests that the synthetic data are practically usable regardless of their modality type.
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