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2018 | OriginalPaper | Chapter

Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression Using Irregularity Age Map in Brain MRI

Authors : Muhammad Febrian Rachmadi, Maria del C. Valdés-Hernández, Taku Komura

Published in: PRedictive Intelligence in MEdicine

Publisher: Springer International Publishing

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Abstract

The Irregularity Age Map (IAM) for the unsupervised assessment of brain white matter hyperintensities (WMH) opens several opportunities in machine learning-based MRI analysis, including transfer task adaptation learning in the segmentation and prediction of brain lesion progression and regression. The lack of need for manual labels is useful for transfer learning. Whereas the nature of IAM itself can be exploited for predicting lesion progression/regression. In this study, we propose the use of task adaptation transfer learning for WMH segmentation using CNN through weakly-training UNet and UResNet using the output from IAM and the use of IAM for predicting patterns of WMH progression and regression.

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previous chapter Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer’s Disease Diagnosis

next chapter Multi-view Brain Network Prediction from a Source View Using Sample Selection via CCA-Based Multi-kernel Connectomic Manifold Learning

https://github.com/febrianrachmadi/iam-tl-progression.

Database can be accessed at http://adni.loni.usc.edu. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

https://github.com/febrianrachmadi/lots-iam-gpu.

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Title: Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression Using Irregularity Age Map in Brain MRI
Authors: Muhammad Febrian Rachmadi
Maria del C. Valdés-Hernández
Taku Komura
Publisher: Springer International Publishing
Book: PRedictive Intelligence in MEdicine
Print ISBN: 978-3-030-00319-7

Electronic ISBN: 978-3-030-00320-3

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00320-3_11

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