Deep Learning for Breast MRI Style Transfer with Limited Training Data.

ABSTRACT: In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is made possible by training our model on unlimited possibilities of simulated random medical imaging styles on the training set, making our work more computationally efficient when compared with other style transfer methods. Moreover, our method enables arbitrary style transfer: transferring images to styles unseen in training. This is useful for medical imaging, where images are acquired using different protocols and different scanner models, resulting in a variety of styles that data may need to be transferred between. Our model disentangles image content from style and can modify an image's style by simply repl

SUBMITTER: Cao S 

PROVIDER: S-EPMC10039216 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature