Multi-level semi-supervised GAN enables accurate segmentation of breast ultrasound images

Ultrasound imaging is a crucial modality for breast cancer diagnosis, with lesion segmentation being an essential component of computer-aided diagnosis systems. However, the high cost and effort associated with pixel-level annotation significantly hinder the advancement of breast ultrasound (BUS) image segmentation algorithms. In this paper, we propose a multi-level semi-supervised generative adversarial network (MLSS-GAN) that incorporates a generator and a novel multi-level classifier acting as the discriminator. While some previous approaches have incorporated multi-level adversarial learning, our discriminator employs an encoder for feature extraction and two decoders designed for distinct image-level and pixel-level adversarial tasks, enabling more comprehensive segmentation supervision. Specifically, one decoder performs image-level classification, while the other conducts pixel-level classification. This multi-level adversarial strategy compels the generator to produce images that closely resemble real BUS images at different feature representations, thereby enriching the classification feature space. To assess the effectiveness of the proposed method, comprehensive comparisons with both fully supervised and semi-supervised approaches are conducted on the BUSI dataset. Experimental results demonstrated the superior performance of our method, especially under conditions with only a limited number of annotated BUS images.