Minuo Qing, Emily X. Ding and Robert J. Hou, Vineyards AI Lab, New Zealand

Artificial Intelligence (AI) has achieved remarkable performance in the field of medical image analysis, particularly in tasks such as object detection, segmentation, and classification. In this paper, we introduce a solution for automatic breast cancer diagnosis based on the U-Net architecture, which we call (U-Net)+. The novel (U-Net)+ is designed to handle both segmentation and classification tasks within a signal framework. We retained the original U-Net architecture due to its strong learning capabilities and its advantages in semantic segmentation. Notable, we incorporated fully connected layers into the bottleneck layers, serving as a multi-functional classifier for both initial diagnoses based on raw images and further diagnoses for segmented images. The (U-Net)+ model is trained using a joint loss function. We conducted the experiments on breast ultrasound images, demonstrating that the (U-Net) performs well in both classification and segmentation tasks.

Breast Cancer; Automatic Diagnose; Classification; Semantic Segmentation; U-Net;