Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection

Bum Chae Kim, Jee Seok Yoon, Jun Sik Choi, Heung Il Suk

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)


Lung cancer is a global and dangerous disease, and its early detection is crucial for reducing the risks of mortality. In this regard, it has been of great interest in developing a computer-aided system for pulmonary nodules detection as early as possible on thoracic CT scans. In general, a nodule detection system involves two steps: (i) candidate nodule detection at a high sensitivity, which captures many false positives and (ii) false positive reduction from candidates. However, due to the high variation of nodule morphological characteristics and the possibility of mistaking them for neighboring organs, candidate nodule detection remains a challenge. In this study, we propose a novel Multi-scale Gradual Integration Convolutional Neural Network (MGI-CNN), designed with three main strategies: (1) to use multi-scale inputs with different levels of contextual information, (2) to use abstract information inherent in different input scales with gradual integration, and (3) to learn multi-stream feature integration in an end-to-end manner. To verify the efficacy of the proposed network, we conducted exhaustive experiments on the LUNA16 challenge datasets by comparing the performance of the proposed method with state-of-the-art methods in the literature. On two candidate subsets of the LUNA16 dataset, i.e., V1 and V2, our method achieved an average CPM of 0.908 (V1) and 0.942 (V2), outperforming comparable methods by a large margin. Our MGI-CNN is implemented in Python using TensorFlow and the source code is available from

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalNeural Networks
Publication statusPublished - 2019 Jul

Bibliographical note

Funding Information:
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-01779 , A machine learning and statistical inference framework for explainable artificial intelligence).

Publisher Copyright:
© 2019 Elsevier Ltd


  • False positive reduction
  • Lung cancer screening
  • Multi-scale convolutional neural network
  • Multi-stream feature integration
  • Pulmonary nodule detection

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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