BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation
This work addresses the problem of improving computer-aided diagnosis for breast cancer by enhancing segmentation accuracy, though it appears incremental as it builds on existing network architectures for medical image segmentation.
The paper tackles the challenge of accurately segmenting malignant breast lesions in ultrasound images, which is difficult due to heterogeneous structures and similar intensity distributions, and proposes BAGNet, a bidirectional aware guidance network that achieves competitive segmentation results compared to state-of-the-art methods on a public dataset.
Breast lesions segmentation is an important step of computer-aided diagnosis system, and it has attracted much attention. However, accurate segmentation of malignant breast lesions is a challenging task due to the effects of heterogeneous structure and similar intensity distributions. In this paper, a novel bidirectional aware guidance network (BAGNet) is proposed to segment the malignant lesion from breast ultrasound images. Specifically, the bidirectional aware guidance network is used to capture the context between global (low-level) and local (high-level) features from the input coarse saliency map. The introduction of the global feature map can reduce the interference of surrounding tissue (background) on the lesion regions. To evaluate the segmentation performance of the network, we compared with several state-of-the-art medical image segmentation methods on the public breast ultrasound dataset using six commonly used evaluation metrics. Extensive experimental results indicate that our method achieves the most competitive segmentation results on malignant breast ultrasound images.