基于概念发现的传染病自动分类分析
Automatic Infectious Disease Classification Analysis with Concept Discovery
摘要：
从图像中自动分类传染病可以方便所需的医疗诊断。这种方法可以查明由于资源限制而诊断不足的疾病，如结核病，以及临床医生在诊断方面缺乏经验或智慧的新疾病，如猴痘。避免漏诊或延误诊断将防止进一步传播并改善临床结果。为了理解和信任神经网络预测，分析学习的表示是必要的。在这项工作中，我们认为，自动发现的概念，即，人类可解释的属性允许对医学图像分析任务中的学习信息的深入理解，从而概括训练标签或协议之外的信息。我们概述了医学图像和计算机视觉领域现有的概念发现方法，并评估了结核病（TB）预测和猴痘预测任务的代表性方法。最后，我们提出了NMFx，一个通用的NMF公式的可解释性的概念发现，在一个统一的方式在无监督，弱监督和监督的情况下工作。
英文摘要：
Automatic infectious disease classification from images can facilitate needed medical diagnoses. Such an approach can identify diseases, like tuberculosis, which remain under-diagnosed due to resource constraints and also novel and emerging diseases, like monkeypox, which clinicians have little experience or acumen in diagnosing. Avoiding missed or delayed diagnoses would prevent further transmission and improve clinical outcomes. In order to understand and trust neural network predictions, analysis of learned representations is necessary. In this work, we argue that automatic discovery of concepts, i.e., human interpretable attributes, allows for a deep understanding of learned information in medical image analysis tasks, generalizing beyond the training labels or protocols. We provide an overview of existing concept discovery approaches in medical image and computer vision communities, and evaluate representative methods on tuberculosis (TB) prediction and monkeypox prediction tasks. Finally, we propose NMFx, a general NMF formulation of interpretability by concept discovery that works in a unified way in unsupervised, weakly supervised, and supervised scenarios.
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。
