CoCa-GAN: Common-Feature-Learning-Based Context-Aware Generative Adversarial Network for Glioma Grading

Pu Huang, Dengwang Li, Zhicheng Jiao, Dongming Wei, Guoshi Li, Qian Wang, Han Zhang, Dinggang Shen

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

14 Citations (Scopus)


Multi-modal structural MRI has been widely used for presurgical glioma grading for treatment planning. Despite providing complementary information, a complete set of high-resolution multi-modality data is costly and often impossible to acquire in clinical settings (although T1 MRI is more commonly acquired). To leverage more comprehensive multimodality information for better glioma grading instead of doing so with T1 MRI data only, we introduce a three-dimensional common feature learning-based context-aware generative adversarial network (CoCa-GAN) for multimodal MRI data synthesis based on T1 MRI and use the comprehensive features from a common feature space to achieve a clinically feasible glioma grading with limited imaging modality. The common feature space is first learned by simultaneously utilizing four MRI modalities with the adversarial learning and context-aware learning, where the inter-modality relationships and lesion-specific features can be explicitly encoded. Then, the domain (modality) invariant information represented in the common space is leveraged to synthesize the missing modalities for a joint prediction of glioma grades (high- vs. low-grade). Furthermore, Gradient-weighted Class Activation Mapping (GradCAM) is utilized to provide interpretability to the factors that contribute to the grading, for potential clinical usage. Results demonstrate that the common feature learning achieves more accurate glioma grading than simply using single modality data and leads to a comparable performance to that with complete modalities as inputs. Our method offers a highly feasible solution to clinical practice where multi-modality data is often unavailable.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9783030322472
Publication statusPublished - 2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

Bibliographical note

Funding Information:
Acknowledgements. ZJ, HZ, and DS were supported in part by an NIH grant AG041721. PH and DL were supported in part by the Taishan Scholars Project of Shandong Province (Tsqn20161023) and the Primary Research and Development Plan of Shandong Province (No. 2018GGX101018).

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.


  • Generative adversarial network
  • Glioma grading
  • Image synthesis
  • MRI

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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