Multi-domain learning by meta-learning: Taking optimal steps in multi-domain loss landscapes by inner-loop learning

Anthony Sicilia, Xingchen Zhao, Davneet S. Minhas, Erin E. O'Connor, Howard J. Aizenstein, William E. Klunk, Dana L. Tudorascu, Seong Jae Hwang

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

1 Citation (Scopus)


We consider a model-agnostic solution to the problem of Multi-Domain Learning (MDL) for multi-modal applications. Many existing MDL techniques are model-dependent solutions which explicitly require nontrivial architectural changes to construct domain-specific modules. Thus, properly applying these MDL techniques for new problems with well-established models, e.g. U-Net for semantic segmentation, may demand various low-level implementation efforts. In this paper, given emerging multi-modal data (e.g., various structural neuroimaging modalities), we aim to enable MDL purely algorithmically so that widely used neural networks can trivially achieve MDL in a model-independent manner. To this end, we consider a weighted loss function and extend it to an effective procedure by employing techniques from the recently active area of learning-to-learn (meta-learning). Specifically, we take inner-loop gradient steps to dynamically estimate posterior distributions over the hyperparameters of our loss function. Thus, our method is model-agnostic, requiring no additional model parameters and no network architecture changes; instead, only a few efficient algorithmic modifications are needed to improve performance in MDL. We demonstrate our solution to a fitting problem in medical imaging, specifically, in the automatic segmentation of white matter hyperintensity (WMH). We look at two neuroimaging modalities (T1-MR and FLAIR) with complementary information fitting for our problem.

Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781665412469
Publication statusPublished - 2021 Apr 13
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: 2021 Apr 132021 Apr 16

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021

Bibliographical note

Funding Information:
This work was supported by the NIH/NIA (R01 AG063752, RF1 AG025516, P01 AG025204, K23 MH118070), and SCI Undergraduate Research Scholars Award. We report no conflicts of interests.

Publisher Copyright:
© 2021 IEEE.

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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