论文标题
通过半监督对象检测在OCT中的病变定位
Lesion Localization in OCT by Semi-Supervised Object Detection
论文作者
论文摘要
全球超过3亿人受到各种视网膜疾病的影响。通过非侵入性光学相干断层扫描（OCT）扫描，可以确定视网膜的许多异常结构变化，即视网膜病变。因此，OCT中的自动病变定位对于在早期检测视网膜疾病很重要。为了征服缺乏对深度监督学习的手动注释，本文介绍了一项有关使用半监督对象检测（SSOD）在OCT图像中进行病变定位的研究。为此，我们开发了一种分类法，以提供当前SSOD方法的统一和结构化的观点，从而确定这些方法中的关键模块。为了评估这些模块在新任务中的影响，我们构建了Oct-SS，这是一个由超过1K专家标记的OCT B-SCAN图像和超过13K未标记的B-Scans组成的新数据集。对Oct-SS的广泛实验将无偏见的教师（UNT）确定为病变定位的最佳SSOD方法。此外，我们在这一强大的基线上有所改善，地图从49.34增加到50.86。
Over 300 million people worldwide are affected by various retinal diseases. By noninvasive Optical Coherence Tomography (OCT) scans, a number of abnormal structural changes in the retina, namely retinal lesions, can be identified. Automated lesion localization in OCT is thus important for detecting retinal diseases at their early stage. To conquer the lack of manual annotation for deep supervised learning, this paper presents a first study on utilizing semi-supervised object detection (SSOD) for lesion localization in OCT images. To that end, we develop a taxonomy to provide a unified and structured viewpoint of the current SSOD methods, and consequently identify key modules in these methods. To evaluate the influence of these modules in the new task, we build OCT-SS, a new dataset consisting of over 1k expert-labeled OCT B-scan images and over 13k unlabeled B-scans. Extensive experiments on OCT-SS identify Unbiased Teacher (UnT) as the best current SSOD method for lesion localization. Moreover, we improve over this strong baseline, with mAP increased from 49.34 to 50.86.
