How to learn from unlabeled volume data: Self-Supervised 3D Context Feature LearningDownload PDF


23 May 2019 (modified: 28 Jun 2019)OpenReview Anonymous Preprint Blind SubmissionReaders: Everyone
Abstract: The vast majority of 3D medical images lacks detailed image-based expert annotations. The ongoing advances of deep convolutional neural networks clearly demonstrate the benefit of supervised learning to successfully extract relevant anatomical information and aid image-based analysis and interventions, but it heavily relies on labeled data. Self-supervised learning, that requires no expert labels, provides an appealing way to discover data-inherent patterns and leverage anatomical information freely available from medical images themselves. In this work, we propose a new approach to train effective convolutional feature extractors based on a new concept of image-intrinsic spatial offset relations with an auxiliary heatmap regression loss. The learned features successfully capture semantic, anatomical information and enable state-of-the-art accuracy for a k-NN based one-shot segmentation task without any subsequent fine-tuning.
Keywords: Self-Supervised Learning, Volumetric Image Segmentation
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