论文标题
微笑甘斯：通过gan的半监督聚类从医学图像中解剖脑疾病异质性
Smile-GANs: Semi-supervised clustering via GANs for dissecting brain disease heterogeneity from medical images
论文作者
论文摘要
应用于复杂的生物医学数据的机器学习方法使诊断/预后价值的疾病特征的构建。但是，对理解疾病异质性的关注减少了。半监督的聚类方法可以通过估计（例如健康）对照组（CN）组到患者（PT）组的多个转换来解决此问题，以寻求捕获基本途径过程的异质性。本文中，我们提出了一种新颖的方法，微笑甘恩（通过gans的半监督聚类），用于半监督聚类，并将其应用于大脑MRI扫描。 Smile-Gans首先通过从CN产生PT来学习多个不同的映射，每个映射都表征了一种相对不同的病理模式。此外，聚类模型通过映射函数进行交互性训练，以将PT分配到相应的亚型成员身份。 Smile-Gans使用PT/CN数据分布上的松弛假设并施加映射非线性，捕获了CN和PT域之间的分布分布的异质差异。我们首先使用模拟数据验证了微笑甘恩，随后在真实数据上证明了其表征阿尔茨海默氏病（AD）（AD）及其前驱相的异质性的潜力。该模型首先是使用来自ADNI2数据库的基线MRI训练的，然后应用于ADNI1和BLSA的纵向数据。发现了四个具有不同神经解剖学模式的强大亚型：1）正常大脑，2）AD非典型的弥漫性萎缩，3）局灶性内侧颞叶萎缩，4）典型AD。进一步的纵向分析发现了两种从前瞻性到完整AD的截然不同的渐进式途径：i）亚型1-2-4和ii）亚型1-3-4。
Machine learning methods applied to complex biomedical data has enabled the construction of disease signatures of diagnostic/prognostic value. However, less attention has been given to understanding disease heterogeneity. Semi-supervised clustering methods can address this problem by estimating multiple transformations from a (e.g. healthy) control (CN) group to a patient (PT) group, seeking to capture the heterogeneity of underlying pathlogic processes. Herein, we propose a novel method, Smile-GANs (SeMi-supervIsed cLustEring via GANs), for semi-supervised clustering, and apply it to brain MRI scans. Smile-GANs first learns multiple distinct mappings by generating PT from CN, with each mapping characterizing one relatively distinct pathological pattern. Moreover, a clustering model is trained interactively with mapping functions to assign PT into corresponding subtype memberships. Using relaxed assumptions on PT/CN data distribution and imposing mapping non-linearity, Smile-GANs captures heterogeneous differences in distribution between the CN and PT domains. We first validate Smile-GANs using simulated data, subsequently on real data, by demonstrating its potential in characterizing heterogeneity in Alzheimer's Disease (AD) and its prodromal phases. The model was first trained using baseline MRIs from the ADNI2 database and then applied to longitudinal data from ADNI1 and BLSA. Four robust subtypes with distinct neuroanatomical patterns were discovered: 1) normal brain, 2) diffuse atrophy atypical of AD, 3) focal medial temporal lobe atrophy, 4) typical-AD. Further longitudinal analyses discover two distinct progressive pathways from prodromal to full AD: i) subtypes 1 - 2 - 4, and ii) subtypes 1 - 3 - 4. Although demonstrated on an important biomedical problem, Smile-GANs is general and can find application in many biomedical and other domains.
