Abstract
This paper addresses the issue of fusing datasets coming from different imaging protocols or scanners to boost the performance of computer aided diagnostic system. We present novel contributions in the field of subspace alignment methods that are part of domain adaptation framework. We first introduce a simple approach based on scaling the features of the different distribution and accounting for the class information. We also extend an unsupervised landmark based approach that has been recently developed to the supervised setting. These methods are evaluated in the context of prostate cancer screening based on two patient MRI databases acquired on different scanners. We demonstrate promising performance of the scaling based method when both databases contain similar number of annotated samples, and stable performance of the landmark based method even with unbalanced datasets.
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Aljundi, R., Lehaire, J., Prost-Boucle, F., Rouvière, O., Lartizien, C. (2015). Transfer Learning for Prostate Cancer Mapping Based on Multicentric MR Imaging Databases. In: Bhatia, K., Lombaert, H. (eds) Machine Learning Meets Medical Imaging. MLMMI 2015. Lecture Notes in Computer Science(), vol 9487. Springer, Cham. https://doi.org/10.1007/978-3-319-27929-9_8
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DOI: https://doi.org/10.1007/978-3-319-27929-9_8
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