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Pulmonary nodule detection, false positive reduction and segmentationrepresent three of the most common tasks in the computeraided analysis of chestCT images. Methods have been proposed for eachtask with deep learning basedmethods heavily favored recently. However training deep learning models tosolve each task separately may be sub-optimal - resource intensive and withoutthe benefit of feature sharing. Here, we propose a new end-to-end 3D deepconvolutional neural net (DCNN), called NoduleNet, to solve nodule detection,false positive reduction and nodule segmentation jointly in a multi-taskfashion. To avoid friction between different tasks and encourage featurediversification, we incorporate two major design tricks: 1) decoupled featuremaps for nodule detection and false positive reduction, and 2) a segmentationrefinement subnet for increasing the precision of nodule segmentation.Extensive experiments on the large-scale LIDC dataset demonstrate that themulti-task training is highly beneficial, improving the nodule detectionaccuracy by 10.27%, compared to the baseline model trained to only solve thenodule detection task. We also carry out systematic ablation studies tohighlight contributions from each of the added components. Code is available athttps://github.com/uci-cbcl/NoduleNet.

NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation