The acquisition of large-scale medical image data, necessary for training machine learning algorithms, is hampered by associated expert-driven annotation costs. Mining hospital archives can address this problem, but labels often incomplete or noisy, e.g ., 50% of the lesions in DeepLesion are left unlabeled. Thus, effective label harvesting methods are critical. This is the goal of our work, where we introduce Lesion-Harvester—a powerful system to harvest missing annotations from lesion datasets at high precision. Accepting the need for some degree of expert labor, we use a small fully-labeled image subset to intelligently mine annotations from the remainder. To do this, we chain together a highly sensitive lesion proposal generator (LPG) and a very selective lesion proposal classifier (LPC). Using a new hard negative suppression loss, the resulting harvested and hard-negative proposals are then employed to iteratively finetune our LPG. While our framework is generic, we optimize our performance by proposing a new 3D contextual LPG and by using a global-local multi-view LPC. Experiments on DeepLesion demonstrate that Lesion-Harvester can discover an additional 9,805 lesions at a precision of 90%. We publicly release the harvested lesions, along with a new test set of completely annotated DeepLesion volumes. We also present a pseudo 3D IoU evaluation metric that corresponds much better to the real 3D IoU than current DeepLesion evaluation metrics. To quantify the downstream benefits of Lesion-Harvester we show that augmenting the DeepLesion annotations with our harvested lesions allows state-of-the-art detectors to boost their average precision by 7 to 10%.

中文翻译：

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病灶收割：大规模地开采未标记的病灶和难治性病例。

训练机器学习算法所需的大规模医学图像数据的获取受到相关专家驱动的注释成本的阻碍。采矿医院的档案可以解决这个问题，但标签通常不完整或嘈杂，例如 .. DeepLesion中50％的病变未标记。因此，有效的标签收集方法至关重要。这是我们工作的目标，我们引入了Lesion-Harvester，这是一个功能强大的系统，可以高精度地从病变数据集中收集缺失的注释。接受了某种程度的专家工作的需要，我们使用一个全标签的小图像子集来智能地挖掘其余部分的注释。为此，我们将高度敏感的病变提议生成器（LPG）和高度选择性的病变提议分类器（LPC）链接在一起。使用新的硬消极抑制损失，然后将所得的收获和硬消极提议用于迭代微调我们的LPG。虽然我们的框架是通用的，但我们通过提出新的3D上下文LPG并使用全局局部多视图LPC来优化性能。DeepLesion上的实验表明，Lesion-Harvester可以以90％的精度发现另外的9,805个病变。我们公开发布收获的病灶，以及一套新的完全地带注释的DeepLesion卷。我们还提出了一种伪3D IoU评估指标，它比当前的DeepLesion评估指标更好地对应于真实3D IoU。为了量化Lesion-Harvester的下游效益，我们表明，利用我们收获的病变来增强DeepLesion注释，可使最先进的探测器将其平均精度提高7％至10％。