Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsDownload PDF

25 Jan 2020, 12:35 (edited 26 Jun 2020)MIDL 2020 Conference Blind SubmissionReaders: Everyone
  • Keywords: Brain Tumor Segmentation, Brain lesion segmentation, Transfer Learning, Variational Inference, Bayesian Neural Networks, Variational Autoencoder, 3D CNN
  • TL;DR: Transfer learning for DNN based segmentation between illnesses by learning generative prior in conv-filter space is better than pretrain.
  • Track: short paper
  • Paper Type: methodological development
  • Abstract: Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods. Nevertheless, these methods are inapplicable for small datasets, which are very common in medical problems. To this end, we propose a knowledge transfer method between diseases via the Generative Bayesian Prior network. Our approach is compared to a pre-train approach and random initialization and obtains the best results in terms of Dice Similarity Coefficient metric for the small subsets of the Brain Tumor Segmentation 2018 database (BRATS2018).
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