Deep adversarial learning for multi-modality missing data completion

Lei Cai, Zhengyang Wang, Hongyang Gao, Dinggang Shen, Shuiwang Ji

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

9 Citations (Scopus)

Abstract

Multi-modality data are widely used in clinical applications, such as tumor detection and brain disease diagnosis. Different modalities can usually provide complementary information, which commonly leads to improved performance. However, some modalities are commonly missing for some subjects due to various technical and practical reasons. As a result, multi-modality data are usually incomplete, raising the multi-modality missing data completion problem. In this work, we formulate the problem as a conditional image generation task and propose an encoder-decoder deep neural network to tackle this problem. Specifically, the model takes the existing modality as input and generates the missing modality. By employing an auxiliary adversarial loss, our model is able to generate high-quality missing modality images. At the same time, we propose to incorporate the available category information of subjects in training to enable the model to generate more informative images. We evaluate our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, where positron emission tomography (PET) modalities are missing. Experimental results show that the trained network can generate high-quality PET modalities based on existing magnetic resonance imaging (MRI) modalities, and provide complementary information to improve the detection and tracking of the Alzheimer's disease. Our results also show that the proposed methods generate higher quality images than baseline methods as measured by various image quality statistics.

Original languageEnglish
Title of host publicationKDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1158-1166
Number of pages9
ISBN (Print)9781450355520
DOIs
Publication statusPublished - 2018 Jul 19
Externally publishedYes
Event24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 - London, United Kingdom
Duration: 2018 Aug 192018 Aug 23

Other

Other24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
CountryUnited Kingdom
CityLondon
Period18/8/1918/8/23

Keywords

  • Adversarial loss function
  • Deep learning
  • Disease diagnosis
  • Missing data completion

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Cai, L., Wang, Z., Gao, H., Shen, D., & Ji, S. (2018). Deep adversarial learning for multi-modality missing data completion. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1158-1166). Association for Computing Machinery. https://doi.org/10.1145/3219819.3219963