BACKGROUND:Calcification is an important criterion for classification between benign and malignant thyroid nodules. Deep learning provides an important means for automatic calcification recognition, but it is tedious to annotate pixel-level labels for calcifications with various morphologies. OBJECTIVE:This study aims to improve accuracy of calcification recognition and prediction of its location, as well as to reduce the number of pixel-level labels in model training. METHODS:We proposed a collaborative supervision network based on attention gating (CS-AGnet), which was composed of two branches: a segmentation network and a classification network. The reorganized two-stage collaborative semi-supervised model was trained under the supervision of all image-level labels and few pixel-level labels. RESULTS:The results show that although our semi-supervised network used only 30% (289 cases) of pixel-level labels for training, the accuracy of calcification recognition reaches 92.1%, which is very close to 92.9% of deep supervision with 100% (966 cases) pixel-level labels. The CS-AGnet enables to focus the model’s attention on calcification objects. Thus, it achieves higher accuracy than other deep learning methods. CONCLUSIONS:our collaborative semi-supervised model has a preferable performance in calcification recognition, and it reduces the number of manual annotations of pixel-level labels. Moreover, it may be of great reference for the object recognition of medical dataset with few labels.

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基于超声图像注意门控协同监督网络的甲状腺结节钙化识别[J].

背景：钙化是区分甲状腺结节良恶性的重要标准。深度学习为自动钙化识别提供了重要手段，但为各种形态的钙化标注像素级标签是一件繁琐的工作。目的：本研究旨在提高钙化识别和位置预测的准确性，并减少模型训练中像素级标签的数量。方法：我们提出了一种基于注意力门控的协同监督网络（CS-AGnet），它由两个分支组成：分割网络和分类网络。重组后的两阶段协作半监督模型在所有图像级标签和少量像素级标签的监督下进行训练。结果：结果表明，虽然我们的半监督网络只使用了 30%（289 个案例）的像素级标签进行训练，但钙化识别的准确率达到了 92.1%，非常接近深度监督的 92.9%，100%（966案例）像素级标签。CS-AGnet 能够将模型的注意力集中在钙化对象上。因此，它实现了比其他深度学习方法更高的准确率。结论：我们的协作半监督模型在钙化识别方面具有较好的性能，并且减少了像素级标签的手动注释次数。此外，它可能对标签较少的医学数据集的对象识别有很大的参考价值。9% 的深度监督，100%（966 个案例）像素级标签。CS-AGnet 能够将模型的注意力集中在钙化对象上。因此，它实现了比其他深度学习方法更高的准确率。结论：我们的协作半监督模型在钙化识别方面具有较好的性能，并且减少了像素级标签的手动注释次数。此外，它可能对标签较少的医学数据集的对象识别有很大的参考价值。9% 的深度监督，100%（966 个案例）像素级标签。CS-AGnet 能够将模型的注意力集中在钙化对象上。因此，它实现了比其他深度学习方法更高的准确率。结论：我们的协作半监督模型在钙化识别方面具有较好的性能，并且减少了像素级标签的手动注释次数。此外，它可能对标签较少的医学数据集的对象识别有很大的参考价值。它减少了像素级标签的手动注释数量。此外，它可能对标签较少的医学数据集的对象识别有很大的参考价值。它减少了像素级标签的手动注释数量。此外，它可能对标签较少的医学数据集的对象识别有很大的参考价值。