3D Aggregated Faster R-CNN for General Lesion Detection

• URL: http://arxiv.org/abs/2001.11071v1
• Date: Wed, 29 Jan 2020 19:57:35 GMT
• Title: 3D Aggregated Faster R-CNN for General Lesion Detection
• Authors: Ning Zhang, Yu Cao, Benyuan Liu, and Yan Luo
• Abstract summary: This paper implements an end-to-end 3D Aggregated Faster R-CNN solution. We demonstrate our model can achieve the state of the art performance on both LUNA16 and DeepLesion dataset.
• Score: 20.43919148873075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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