Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) of breast cancers. In an automaticCAD system, lesion detection is critical for the following diagnosis. However, existing DL-based methods generally require voluminous manually-annotated region of interest (ROI) labels and class labels to train both the lesion detection and diagnosis models. In clinical practice, the ROI labels, i.e. ground truths, may not always be optimal for the classification task due to individual experience of sonologists, resulting in the issue of coarse annotation that limits the diagnosis performance of a CAD model. To address this issue, a novel Two-Stage Detection and Diagnosis Network (TSDDNet) is proposed based on weakly supervised learning to enhance diagnostic accuracy of the ultrasound-based CAD for breast cancers. In particular, all the ROI-level labels are considered as coarse labels in the first training stage, and then a candidate selection mechanism is designed to identify optimallesion areas for both the fully and partially annotated samples. It refines the current ROI-level labels in the fully annotated images and the detected ROIs in the partially annotated samples with a weakly supervised manner under the guidance of class labels. In the second training stage, a self-distillation strategy further is further proposed to integrate the detection network and classification network into a unified framework as the final CAD model for joint optimization, which then further improves the diagnosis performance. The proposed TSDDNet is evaluated on a B-mode ultrasound dataset, and the experimental results show that it achieves the best performance on both lesion detection and diagnosis tasks, suggesting promising application potential.

翻译：深度学习在基于超声的乳腺癌计算机辅助诊断中已展现出高效性。在自动诊断系统中，病灶检测对后续诊断至关重要。然而，现有深度学习方法通常需要大量人工标注的感兴趣区域标签和类别标签来训练病灶检测与诊断模型。临床实践中，由于超声医师个体经验差异，作为真值的感兴趣区域标签可能并非分类任务的最优标注，导致粗标注问题限制了计算机辅助诊断模型的诊断性能。为解决此问题，提出基于弱监督学习的双阶段检测与诊断网络，通过提升乳腺癌超声诊断模型的准确率。具体而言，第一阶段将所有感兴趣区域级标签视为粗标签，并设计候选区域选择机制以确定完全标注样本和部分标注样本中的最优病灶区域。该机制在类别标签引导下，以弱监督方式优化完全标注图像中的现有感兴趣区域标签及部分标注样本中的检测区域。第二阶段进一步提出自蒸馏策略，将检测网络与分类网络整合为统一框架，构建用于联合优化的最终计算机辅助诊断模型，从而进一步提升诊断性能。在B型超声数据集上的实验结果表明，该网络在病灶检测与诊断任务中均取得最优性能，展现出良好的应用潜力。