论文标题
3D汇总的更快的R-CNN用于一般病变检测
3D Aggregated Faster R-CNN for General Lesion Detection
论文作者
论文摘要
病变是人体组织中的损害和异常。他们中的许多人以后可能会变成癌症等致命疾病。检测病变对于早期诊断和及时治疗至关重要。为此，计算机断层扫描（CT）扫描通常是筛选工具，使我们能够利用现代对象检测技术来检测病变。但是，CT扫描中的病变通常很小且稀疏。病变的局部区域可能会令人困惑，导致更快的R-CNN的基于区域的分类器分支很容易失败。因此，大多数现有的最新解决方案分别训练两种类型的异质网络（多相），以分别用于候选生成和假阳性减少（FPR）的目的。在本文中，我们通过在RPN的骨架上堆叠“聚合的分类器分支”来强制执行端到端3D汇总的更快的R-CNN解决方案。该分类器分支配备了特征聚合和局部放大层，以增强分类器分支。我们证明我们的模型可以在Luna16和DeepLeperion数据集上实现最先进的性能。尤其是，我们在LUNA16上实现了最好的单模单建模FOR性能，每次加工扫描的推理时间为4.2s。
Lesions are damages and abnormalities in tissues of the human body. Many of them can later turn into fatal diseases such as cancers. Detecting lesions are of great importance for early diagnosis and timely treatment. To this end, Computed Tomography (CT) scans often serve as the screening tool, allowing us to leverage the modern object detection techniques to detect the lesions. However, lesions in CT scans are often small and sparse. The local area of lesions can be very confusing, leading the region based classifier branch of Faster R-CNN easily fail. Therefore, most of the existing state-of-the-art solutions train two types of heterogeneous networks (multi-phase) separately for the candidate generation and the False Positive Reduction (FPR) purposes. In this paper, we enforce an end-to-end 3D Aggregated Faster R-CNN solution by stacking an "aggregated classifier branch" on the backbone of RPN. This classifier branch is equipped with Feature Aggregation and Local Magnification Layers to enhance the classifier branch. We demonstrate our model can achieve the state of the art performance on both LUNA16 and DeepLesion dataset. Especially, we achieve the best single-model FROC performance on LUNA16 with the inference time being 4.2s per processed scan.
