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Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation

  • Wenzhe Wang
  • Yifei Lu
  • Bian Wu
  • Tingting Chen
  • Danny Z. Chen
  • Jian WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Automatic and accurate pulmonary nodule segmentation in lung Computed Tomography (CT) volumes plays an important role in computer-aided diagnosis of lung cancer. However, this task is challenging due to target/background voxel imbalance and the lack of voxel-level annotation. In this paper, we propose a novel deep region-based network, called Nodule R-CNN, for efficiently detecting pulmonary nodules in 3D CT images while simultaneously generating a segmentation mask for each instance. Also, we propose a novel Deep Active Self-paced Learning (DASL) strategy to reduce annotation effort and also make use of unannotated samples, based on a combination of Active Learning and Self-Paced Learning (SPL) schemes. Experimental results on the public LIDC-IDRI dataset show our Nodule R-CNN achieves state-of-the-art results on pulmonary nodule segmentation, and Nodule R-CNN trained with the DASL strategy performs much better than Nodule R-CNN trained without DASL using the same amount of annotated samples.

Notes

Acknowledgement

The research of D.Z. Chen was supported in part by NSF Grant CCF-1617735.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wenzhe Wang
    • 1
  • Yifei Lu
    • 1
  • Bian Wu
    • 2
  • Tingting Chen
    • 1
  • Danny Z. Chen
    • 3
  • Jian Wu
    • 1
    Email author
  1. 1.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
  2. 2.Data Science and AI LabWeDoctor Group LimitedHangzhouChina
  3. 3.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA

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