Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders

• Published in: IEEE transactions on medical imaging Vol. 38; no. 12; pp. 2807 - 2820
• Main Authors: Pesteie, Mehran, Abolmaesumi, Purang, Rohling, Robert N.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Biomedical imaging; Brain; center-line identification; Coders; Computational modeling; conditional variational auto-encoder; Data acquisition; Data augmentation; Data models; Datasets; Deep learning; Feature extraction; Image classification; Image processing; Image segmentation; Labels; magnetic resonance; Magnetic resonance imaging; Medical imaging; Model accuracy; Probability distribution; Spine; Synthesis; Training; tumor segmentation; Ultrasound
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2019.2914656

Current deep supervised learning methods typically require large amounts of labeled data for training. Since there is a significant cost associated with clinical data acquisition and labeling, medical datasets used for training these models are relatively small in size. In this paper, we aim to alleviate this limitation by proposing a variational generative model along with an effective data augmentation approach that utilizes the generative model to synthesize data. In our approach, the model learns the probability distribution of image data conditioned on a latent variable and the corresponding labels. The trained model can then be used to synthesize new images for data augmentation. We demonstrate the effectiveness of the approach on two independent clinical datasets consisting of ultrasound images of the spine and magnetic resonance images of the brain. For the spine dataset, a baseline and a residual model achieve an accuracy of 85% and 92%, respectively, using our method compared to 78% and 83% using a conventional training approach for image classification task. For the brain dataset, a baseline and a U-net network achieve an accuracy of 84% and 88%, respectively, in Dice coefficient in tumor segmentation compared to 80% and 83% for the convention training approach.