An extensive pixel-level augmentation framework for unsupervised cross-modality domain adaptation

Abstract:

Convolutional neural networks (CNNs) have achieved great success in automating the segmentation of medical images. Nevertheless, when a trained CNN is tested on a new domain there is a performance degradation due to the distribution shift. In this work, we present an unsupervised Extensive Pixel-level Augmentation framework (EPA) for cross-modality domain adaptation. EPA implements a two-phase image- and feature-level adaptation method. In the first phase, the source domain images are mapped to target domain in pixel space using the CycleGAN, StAC-DA, and CUT translation models. This creates an augmented translated dataset 3 times bigger than the original. In phase 2, a deeply supervised U-Net network is trained to segment the target images using a semi-supervised adversarial learning approach. In particular, a set of discriminator networks are trained to distinguish between the target and …

Año de publicación:

2023

Keywords:

Fuente:

google