Breast cancer poses a significant threat to women's health, and early diagnosis is crucial for reducing mortality rates. Automatic breast tumor segmentation is important in medical image processing, but existing methods face challenges with breast pathology images due to sample scarcity, image degradation after data augmentation, and limitations in feature extraction. Traditional networks like U-Net often lose small lesions and edge details during downsampling and struggle with complex images and class imbalance. To address these issues, this study proposes DetailEdgeSkipBalance-Net (DESB-Net), an improved segmentation model based on U-Net. DESB-Net includes several innovations: Enhanced Detail-aware Multi-scale Re-parameterized Convolution (EDConv) for enhanced feature extraction, HistoEdge Focus Module (HEFM) for edge enhancement, Multi-Path Fusion Module (MPFM) for multi-scale feature fusion, and Binary Cross-Entropy Dice Loss (BD Loss) to balance class imbalance and boundary accuracy. These improvements significantly enhance the model's ability to capture small lesions and edge details, improve segmentation accuracy and robustness, and maintain high computational efficiency. On the UCSB dataset, DESB-Net achieved an mIoU of 79.53% and accuracy of 97.02%, outperforming U-Net by 6.5% and 1.89%, respectively, without increasing parameters or computational load. On the BCSS dataset, it achieved an mIoU of 63.4% and accuracy of 85.8%, surpassing U-Net by 4.2% and 2.6%. DESB-Net also outperformed mainstream models like DeepLabv3+, SegFormer, ResUNet, and Connected-UNets, demonstrating its effectiveness in breast pathology image segmentation. These results highlight the potential of DESB-Net to improve diagnostic accuracy and efficiency in clinical settings, making it a promising tool for early detection and treatment of breast cancer.
Liu et al. (Mon,) studied this question.