# Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems

## TL;DR

This paper introduces a Bayesian generative model for transferring knowledge in MRI segmentation, improving performance on small datasets common in medical imaging.

## Contribution

The paper presents a novel Bayesian generative prior network for knowledge transfer, outperforming pre-training and random initialization in small dataset MRI segmentation tasks.

## Key findings

- Achieved higher Dice Similarity Coefficient on BRATS2018 small subsets.
- Outperformed pre-train and random initialization methods.
- Demonstrated effectiveness of Bayesian transfer in medical image segmentation.

## 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).