TY - JOUR AU - Kuzina, Anna AU - Egorov, Evgenii AU - Burnaev, Evgeny PY - 2019 M3 - Original Research TI - Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems JO - Frontiers in Neuroscience UR - https://www.frontiersin.org/articles/10.3389/fnins.2019.00844 VL - 13 SN - 1662-453X N2 - 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). ER -