学习用于异常检测的图像表示：在药物开发中发现组织学改变的应用
Learning image representations for anomaly detection: application to discovery of histological alterations in drug development
AI总结：
提出一种基于辅助任务训练CNN并结合中心损失的组织病理学图像异常检测系统，用于早期药物毒性评估以减少后期药物流失。
摘要：
我们提出了一种用于组织病理学图像异常检测的系统。在组织学中，正常样本通常很丰富，而异常（病理性）病例稀少或不可用。在此设置下，在健康数据上训练的单类分类器可以检测分布外的异常样本。结合预训练的卷积神经网络（CNN）图像表示的此类方法此前已被用于异常检测（AD）。然而，现成的预训练CNN表示可能对组织中的异常状况不敏感，而健康组织的自然变异可能导致相距较远的表示。为了使表示适应健康组织中的相关细节，我们提出在辅助任务上训练CNN，该任务区分不同物种、器官和染色试剂的健康组织。几乎不需要额外的标注工作量，因为健康样本自动带有上述标签。在训练过程中，我们通过中心损失项强制紧凑的图像表示，这进一步改善了AD的表示。所提出的系统在已发表的肝脏异常数据集上优于现有的AD方法。此外，它提供了与专门为量化肝脏异常而设计的传统方法相当的结果。我们表明，我们的方法可用于候选药物在早期开发阶段的毒性评估，从而可能减少昂贵的后期药物流失。
英文摘要：
We present a system for anomaly detection in histopathological images. In histology, normal samples are usually abundant, whereas anomalous (pathological) cases are scarce or not available. Under such settings, one-class classifiers trained on healthy data can detect out-of-distribution anomalous samples. Such approaches combined with pre-trained Convolutional Neural Network (CNN) representations of images were previously employed for anomaly detection (AD). However, pre-trained off-the-shelf CNN representations may not be sensitive to abnormal conditions in tissues, while natural variations of healthy tissue may result in distant representations. To adapt representations to relevant details in healthy tissue we propose training a CNN on an auxiliary task that discriminates healthy tissue of different species, organs, and staining reagents. Almost no additional labeling workload is required, since healthy samples come automatically with aforementioned labels. During training we enforce compact image representations with a center-loss term, which further improves representations for AD. The proposed system outperforms established AD methods on a published dataset of liver anomalies. Moreover, it provided comparable results to conventional methods specifically tailored for quantification of liver anomalies. We show that our approach can be used for toxicity assessment of candidate drugs at early development stages and thereby may reduce expensive late-stage drug attrition.
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