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


Rachmadi M. F. , Valdes-Hernandez M. d. C. , Komura T.

1st International Workshop on PRedictive Intelligence in MEdicine (PRIME), Granada, Nicaragua, 16 September 2018, vol.11121, pp.85-93 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 11121
  • Doi Number: 10.1007/978-3-030-00320-3_11
  • City: Granada
  • Country: Nicaragua
  • Page Numbers: pp.85-93

Abstract

The Irregularity AgeMap (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.