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Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks

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Document pages: 14 pages

Abstract: We investigate the use of image-and-spatial transformer networks (ISTNs) totackle domain shift in multi-site medical imaging data. Commonly, domainadaptation (DA) is performed with little regard for explainability of theinter-domain transformation and is often conducted at the feature-level in thelatent space. We employ ISTNs for DA at the image-level which constrainstransformations to explainable appearance and shape changes. Asproof-of-concept we demonstrate that ISTNs can be trained adversarially on aclassification problem with simulated 2D data. For real-data validation, weconstruct two 3D brain MRI datasets from the Cam-CAN and UK Biobank studies toinvestigate domain shift due to acquisition and population differences. We showthat age regression and sex classification models trained on ISTN outputimprove generalization when training on data from one and testing on the othersite.

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