Automatic lesion detection from computed tomography (CT) scans is an important task in medical diagnosis. However, three frequent properties of medical data make CT lesion detection a challenging task: (1) Scale variance: Large scale variation is across lesion instances. Especially, it is extremely difficult to detect small lesions; (2) Imbalanced data: The data distributions are highly imbalanced, where few classes account for the majority of data; (3) Prediction stability: Based on our observations, an input lesion image with slightly pixel shift or translation can lead to drastic output mispredictions and this is not allowed for medical applications. To address these challenges, this paper proposes a Robust One-Stage Network (ROSNet) for robust CT lesion detection. Specifically, a novel nested structure of neural networks is developed to generate a series of feature pyramids for detecting CT lesions in various scales, an effective data sensitive class-balanced loss as well as a shift-invariant downsampling strategy are also introduced to improve the detection performance. Experiments are conducted on a large-scale and diverse dataset, DeepLesion, showing that ROSNet outperforms the best performance in MICCAI 2019 by 3.95% (2-class detection task) and 25.41% (8-class detection task) in terms of mean average precision (mAP).

通过计算机断层扫描（CT）扫描自动检测病变是医学诊断中的重要任务。但是，医学数据的三个常见属性使CT病变检测成为一项艰巨的任务：（1）比例差异：整个病变实例均存在较大比例差异。特别是，很难检测到小的病变。（2）数据不平衡：数据分布高度不平衡，其中很少的类占大多数数据；（3）预测稳定性：根据我们的观察，输入病灶图像的像素偏移或平移会稍有变化，可能会导致严重的输出错误预测，这在医疗应用中是不允许的。为了解决这些挑战，本文提出了一种健壮的单阶段网络（ROSNet），用于健壮的CT病变检测。具体而言，开发了一种新颖的神经网络嵌套结构以生成一系列特征金字塔，以检测各种规模的CT病变，还引入了有效的数据敏感类平衡损失以及平移不变的下采样策略以提高检测效率性能。在大规模多样的数据集DeepLesion上进行的实验表明，ROSNet在MICCAI 2019中的最佳性能要高出3.95％（2级检测任务）和25。