Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization

Haoliang Li, Yu Fei Wang, Renjie Wan, Shiqi Wang, Tie Qiang Li, Alex C. Kot

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

72 Citations (Scopus)


Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks. However, the recent advanced models still require accessing sufficiently large and representative datasets for training, which is often unfeasible in clinically realistic environments. When trained on limited datasets, the deep neural network is lack of generalization capability, as the trained deep neural network on data within a certain distribution (e.g. the data captured by a certain device vendor or patient population) may not be able to generalize to the data with another distribution. In this paper, we introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding with a novel linear-dependency regularization term to capture the shareable information among medical data collected from different domains. As a result, the trained neural network is expected to equip with better generalization capability to the “unseen" medical data. Experimental results on two challenging medical imaging classification tasks indicate that our method can achieve better cross-domain generalization capability compared with state-of-the-art baselines.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 33
EditorsH. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, H. Lin
Number of pages12
ISBN (Electronic)9781713829546
Publication statusPublished - Dec 2020
Event34th Conference on Neural Information Processing Systems, NeurIPS 2020 - Virtual, Online
Duration: 6 Dec 202012 Dec 2020


Conference34th Conference on Neural Information Processing Systems, NeurIPS 2020
Internet address

Scopus Subject Areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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