胸部X光片的解剖驱动病理检测
Anatomy-Driven Pathology Detection on Chest X-rays
AI总结：
针对病理bounding box标注稀缺导致弱监督检测性能受限的问题，提出ADPD方法，利用易标注的解剖区域bounding box作为病理代理，实验证明其解剖级训练和MIL方法在有限样本下优于或匹敌基线方法。
摘要：
病理检测与勾画能够实现对胸部X光片等医学扫描的自动解读，同时提供高度的 explainability 以支持放射科医生做出 informed decisions。然而，标注病理 bounding box 是一项耗时的任务，导致用于此目的的大型公开数据集稀缺。因此，当前方法使用弱监督目标检测从图像级标注中学习病理的（粗略）定位，但由于缺乏 bounding box 监督，其性能受限。为此，我们提出了解剖驱动病理检测（ADPD），该方法使用易于标注的解剖区域 bounding box 作为病理的代理。我们研究了两种训练方法：使用解剖级病理标签的监督训练，以及使用图像级病理标签的多示例学习（MIL）。结果表明，我们的解剖级训练方法在训练样本有限时优于弱监督方法和完全监督检测，且我们的 MIL 方法与两种基线方法具有竞争力，从而证明了我们方法的潜力。
英文摘要：
Pathology detection and delineation enables the automatic interpretation of medical scans such as chest X-rays while providing a high level of explainability to support radiologists in making informed decisions. However, annotating pathology bounding boxes is a time-consuming task such that large public datasets for this purpose are scarce. Current approaches thus use weakly supervised object detection to learn the (rough) localization of pathologies from image-level annotations, which is however limited in performance due to the lack of bounding box supervision. We therefore propose anatomy-driven pathology detection (ADPD), which uses easy-to-annotate bounding boxes of anatomical regions as proxies for pathologies. We study two training approaches: supervised training using anatomy-level pathology labels and multiple instance learning (MIL) with image-level pathology labels. Our results show that our anatomy-level training approach outperforms weakly supervised methods and fully supervised detection with limited training samples, and our MIL approach is competitive with both baseline approaches, therefore demonstrating the potential of our approach.
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