Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) of breast cancers. In an automatic CAD 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 to limit 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 improve diagnostic accuracy of the ultrasound-based CAD for breast cancers. In particular, all the initial ROI-level labels are considered as coarse annotations before model training. In the first training stage, a candidate selection mechanism is then designed to refine manual ROIs in the fully annotated images and generate accurate pseudo-ROIs for the partially annotated images under the guidance of class labels. The training set is updated with more accurate ROI labels for the second training stage. A fusion network is developed to integrate detection network and classification network into a unified end-to-end framework as the final CAD model in the second training stage. A self-distillation strategy is designed on this model for joint optimization to further improves its diagnosis performance. The proposed TSDDNet is evaluated on three B-mode ultrasound datasets, and the experimental results indicate that it achieves the best performance on both lesion detection and diagnosis tasks, suggesting promising application potential.

中文翻译：

---

利用部分注释的超声图像对乳腺癌进行弱监督病变检测和诊断


事实证明，深度学习 (DL) 对于基于超声的乳腺癌计算机辅助诊断 (CAD) 非常有效。在自动 CAD 系统中，病变检测对于后续诊断至关重要。然而，现有的基于深度学习的方法通常需要大量手动注释的感兴趣区域（ROI）标签和类别标签来训练病变检测和诊断模型。在临床实践中，由于超声医师的个人经验，ROI 标签（即基本事实）可能并不总是最适合分类任务，从而导致粗略注释的问题，从而限制了 CAD 模型的诊断性能。为了解决这个问题，提出了一种基于弱监督学习的新型两阶段检测和诊断网络（TSDDNet），以提高基于超声的 CAD 对乳腺癌的诊断准确性。特别是，在模型训练之前，所有初始 ROI 级别标签都被视为粗略注释。在第一个训练阶段，设计候选选择机制来细化完全注释图像中的手动 ROI，并在类标签的指导下为部分注释图像生成准确的伪 ROI。在第二个训练阶段，训练集会更新为更准确的 ROI 标签。开发融合网络，将检测网络和分类网络集成到统一的端到端框架中，作为第二训练阶段的最终CAD模型。在此模型上设计了自蒸馏策略进行联合优化，以进一步提高其诊断性能。 所提出的 TSDDNet 在三个 B 型超声数据集上进行了评估，实验结果表明它在病变检测和诊断任务上均取得了最佳性能，表明其具有广阔的应用潜力。