Typical machine learning frameworks heavily rely on an underlying assumption that training and test data follow the same distribution. In medical imaging which increasingly begun acquiring datasets from multiple sites or scanners, this identical distribution assumption often fails to hold due to systematic variability induced by site or scanner dependent factors. Therefore, we cannot simply expect a model trained on a given dataset to consistently work well, or generalize, on a dataset from another distribution. In this work, we address this problem, investigating the application of machine learning models to unseen medical imaging data. Specifically, we consider the challenging case of Domain Generalization (DG) where we train a model without any knowledge about the testing distribution. That is, we train on samples from a set of distributions (sources) and test on samples from a new, unseen distribution (target). We focus on the task of white matter hyperintensity (WMH) prediction using the multi-site WMH Segmentation Challenge dataset and our local in-house dataset. We identify how two mechanically distinct DG approaches, namely domain adversarial learning and mix-up, have theoretical synergy. Then, we show drastic improvements of WMH prediction on an unseen target domain.
|Title of host publication||2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - 2021 Apr 13|
|Event||18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France|
Duration: 2021 Apr 13 → 2021 Apr 16
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Conference||18th IEEE International Symposium on Biomedical Imaging, ISBI 2021|
|Period||21/4/13 → 21/4/16|
Bibliographical noteFunding Information:
This work was supported by the NIH/NIA (R01 AG063752, RF1 AG025516, P01 AG025204, K23 MH118070), and SCI UR Scholars Award. We report no conflicts of interests.
© 2021 IEEE.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging