Risk stratification of lung nodules using 3D CNN-based multi-task learning

Sarfaraz Hussein*, Kunlin Cao, Qi Song, Ulas Bagci

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

65 Scopus citations


Risk stratification of lung nodules is a task of primary importance in lung cancer diagnosis. Any improvement in robust and accurate nodule characterization can assist in identifying cancer stage, prognosis, and improving treatment planning. In this study, we propose a 3D Convolutional Neural Network (CNN) based nodule characterization strategy. With a completely 3D approach, we utilize the volumetric information from a CT scan which would be otherwise lost in the conventional 2D CNN based approaches. In order to address the need for a large amount of training data for CNN, we resort to transfer learning to obtain highly discriminative features.Moreover, we also acquire the task dependent feature representation for six high-level nodule attributes and fuse this complementary information via a Multi-task learning (MTL) framework. Finally, we propose to incorporate potential disagreement among radiologists while scoring different nodule attributes in a graph regularized sparsemulti-task learning. We evaluated our proposed approach on one of the largest publicly available lung nodule datasets comprising 1018 scans and obtained state-of-the-art results in regressing the malignancy scores.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings
EditorsHongtu Zhu, Marc Niethammer, Martin Styner, Hongtu Zhu, Dinggang Shen, Pew-Thian Yap, Stephen Aylward, Ipek Oguz
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319590493
StatePublished - 2017
Externally publishedYes
Event25th International Conference on Information Processing in Medical Imaging, IPMI 2017 - Boone, United States
Duration: Jun 25 2017Jun 30 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10265 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Information Processing in Medical Imaging, IPMI 2017
Country/TerritoryUnited States


  • 3D convolutional neural network
  • Computed tomography (CT)
  • Computer-aided diagnosis (CAD)
  • Deep learning
  • Lung nodule characterization
  • Multi-task learning
  • Transfer learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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