PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

TY - UNPB

T1 - PADDIT

T2 - Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

AU - Arteaga, Mauricio Orbes

AU - Sørensen, Lauge

AU - Cardoso, Jorge

AU - Modat, Marc

AU - Ourselin, Sebastien

AU - Sommer, Stefan

AU - Nielsen, Mads

AU - Igel, Christian

AU - Pai, Akshay

PY - 2018/10/3

Y1 - 2018/10/3

N2 - For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input. To induce invariance in CNNs to such transformations, we propose Probabilistic Augmentation of Data using Diffeomorphic Image Transformation (PADDIT) -- a systematic framework for generating realistic transformations that can be used to augment data for training CNNs. We show that CNNs trained with PADDIT outperforms CNNs trained without augmentation and with generic augmentation in segmenting white matter hyperintensities from T1 and FLAIR brain MRI scans.

AB - For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input. To induce invariance in CNNs to such transformations, we propose Probabilistic Augmentation of Data using Diffeomorphic Image Transformation (PADDIT) -- a systematic framework for generating realistic transformations that can be used to augment data for training CNNs. We show that CNNs trained with PADDIT outperforms CNNs trained without augmentation and with generic augmentation in segmenting white matter hyperintensities from T1 and FLAIR brain MRI scans.

KW - cs.CV

M3 - Preprint

T3 - arXiv

BT - PADDIT

ER -