تقرير
Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks
| العنوان: | Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks |
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| المؤلفون: | Robinson, R., Dou, Q., Castro, D. C., Kamnitsas, K., de Groot, M., Summers, R. M., Rueckert, D., Glocker, B. |
| المصدر: | Medical Image Computing and Computer-Assisted Intervention (2020), pp. 710-719, LNCS 12267 |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed with little regard for explainability of the inter-domain transformation and is often conducted at the feature-level in the latent space. We employ ISTNs for DA at the image-level which constrains transformations to explainable appearance and shape changes. As proof-of-concept we demonstrate that ISTNs can be trained adversarially on a classification problem with simulated 2D data. For real-data validation, we construct two 3D brain MRI datasets from the Cam-CAN and UK Biobank studies to investigate domain shift due to acquisition and population differences. We show that age regression and sex classification models trained on ISTN output improve generalization when training on data from one and testing on the other site. Comment: Accepted at MICCAI 2020 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-030-59728-3_69 |
| URL الوصول: | http://arxiv.org/abs/2006.16741 |
| رقم الأكسشن: | edsarx.2006.16741 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-030-59728-3_69 |
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