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Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder.

, , , , и . MIDL, том 143 из Proceedings of Machine Learning Research, стр. 497-506. PMLR, (2021)

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Sönke Langner

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An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction., , , , , и . J. Am. Medical Informatics Assoc., 30 (1): 112-119 (2022)Disentangling Factors of Morphological Variation in an Invertible Brain Aging Model., , , , и . MAD@MICCAI, том 13823 из Lecture Notes in Computer Science, стр. 95-107. Springer, (2022)Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging., , , , , , и . IEEE Trans. Medical Imaging, 41 (9): 2331-2347 (2022)Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows., , , , , , и . MLCN/RNO-AI@MICCAI, том 12449 из Lecture Notes in Computer Science, стр. 23-33. Springer, (2020)CFD validation using in-vitro MRI velocity data - Methods for data matching and CFD error quantification., , , , , и . Comput. Biol. Medicine, (2021)Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows., , , , , и . MLCN@MICCAI, том 13001 из Lecture Notes in Computer Science, стр. 23-33. Springer, (2021)Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder., , , , и . MIDL, том 143 из Proceedings of Machine Learning Research, стр. 497-506. PMLR, (2021)