Brainomaly：利用未标注T1加权脑磁共振图像的无监督神经系统疾病检测
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images
AI总结：
针对医学影像标注稀缺、现有无监督神经疾病检测方法未利用配准神经影像专属知识的问题，提出基于GAN的Brainomaly方法，结合未标注混合图像与伪AUC指标，显著提升了阿尔茨海默病与头痛检测性能。
摘要：
在医学影像领域，发挥深度神经网络的能力颇具挑战，因为获取大规模标注数据集难度很大，尤其是针对罕见疾病，标注工作需要耗费高昂的成本、大量时间与精力。在这类场景下，异常检测等无监督疾病检测方法可大幅减少人力投入。\n常规异常检测通常仅基于健康受试者的图像进行学习，但现实场景中的未标注数据集往往混合了健康受试者与患病受试者的图像。近期研究表明，利用这类未标注图像可提升无监督疾病检测与异常检测的效果，但这些方法未运用配准神经影像的专属知识，导致在神经系统疾病检测中表现欠佳。\n为解决这一局限，我们提出Brainomaly——一种专为神经系统疾病检测设计的基于GAN（生成对抗网络）的图像到图像转换方法。Brainomaly不仅提供适配神经影像的定制化图像到图像转换能力，还能利用未标注的混合图像实现更优的神经系统疾病检测效果。此外，我们提出了一种伪AUC（曲线下面积）指标，解决了无标注样本时推理阶段的模型选择问题，进一步提升了Brainomaly的检测性能。\n大量实验与消融研究表明，在使用公开数据集进行阿尔茨海默病检测、使用机构数据集进行头痛检测的任务中，Brainomaly的表现显著优于现有最先进的无监督疾病检测与异常检测方法。代码可从https://github.com/mahfuzmohammad/Brainomaly获取。
英文摘要：
Harnessing the power of deep neural networks in the medical imaging domain is challenging due to the difficulties in acquiring large annotated datasets, especially for rare diseases, which involve high costs, time, and effort for annotation. Unsupervised disease detection methods, such as anomaly detection, can significantly reduce human effort in these scenarios. While anomaly detection typically focuses on learning from images of healthy subjects only, real-world situations often present unannotated datasets with a mixture of healthy and diseased subjects. Recent studies have demonstrated that utilizing such unannotated images can improve unsupervised disease and anomaly detection. However, these methods do not utilize knowledge specific to registered neuroimages, resulting in a subpar performance in neurologic disease detection. To address this limitation, we propose Brainomaly, a GAN-based image-to-image translation method specifically designed for neurologic disease detection. Brainomaly not only offers tailored image-to-image translation suitable for neuroimages but also leverages unannotated mixed images to achieve superior neurologic disease detection. Additionally, we address the issue of model selection for inference without annotated samples by proposing a pseudo-AUC metric, further enhancing Brainomaly's detection performance. Extensive experiments and ablation studies demonstrate that Brainomaly outperforms existing state-of-the-art unsupervised disease and anomaly detection methods by significant margins in Alzheimer's disease detection using a publicly available dataset and headache detection using an institutional dataset. The code is available from https://github.com/mahfuzmohammad/Brainomaly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
