# Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems Source: [Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:40.088Z Authors: Anna Kuzina, Evgenii Egorov, Evgeny Burnaev Published: 2019-08-15T10:27:32.000Z ## 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). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()