肺部结节的计算机断层扫描（CT）征象在指示结节恶性和准确自动分类上起着重要作用，而精确的自动分类有助于医生提高其诊断效率。然而，鲜有涉及结节征象多标签分类（MLC）的相关研究。此外，获取带标签数据的难度也在很大程度上限制了这一研究方向。为解决这些问题，提出了一种结节征象多标签自动分类系统，该系统由一个三维（3D）卷积神经网络（CNN）和一种高效的新半监督学习（SSL）框架组成。我们的实验使用了两个数据集：肺结节分析16（LUNA16），这是一个用于肺结节分类的公共数据集，以及一个包含结节征象的私有数据集。私有数据集包含641个结节，其中454个结节由放射科医生标注了6个重要征象。我们的分类系统由两个主要部分组成：一个3D CNN模型和一种名为不确定性感知自训练框架加一致性正则化（USC）的SSL方法。在该系统中，使用带标签数据进行有监督训练，并同时使用基于不确定性和置信度的策略选择伪标签样本进行无监督训练，从而共同实现模型优化。对于结节征象的多标签分类，我们提出的3D CNN在均值平均精度（mAP）为0.870和均值曲线下面积（AUC）为0.782方面取得了令人满意的结果。在半监督实验中，相比于有监督学习，我们提出的SSL方法将mAP提高了7.6%（从0.730提高到0.806），将均值AUC提高了8.1%（从0.631提高到0.712）；因此，它高效利用了无标签数据，并与最近先进的方法相比，实现了卓越的性能改进。我们用提出的3D CNN实现了肺结节征象的最佳多标签分类。我们提出的SSL方法也为3D医学图像的多标签分类任务中可能存在的有限标签数据提供了高效的解决方案。2023 Quantitative Imaging in Medicine and Surgery.版权所有。

Computed tomography (CT) signs of lung nodules play an important role in indicating lung nodules' malignancy, and accurate automatic classification of these signs can help doctors improve their diagnostic efficiency. However, few relevant studies targeting multilabel classification (MLC) of nodule signs have been conducted. Moreover, difficulty in obtaining labeled data also restricts this avenue of research to a large extent. To address these problems, a multilabel automatic classification system for nodule signs is proposed, which consists of a 3-dimensional (3D) convolutional neural network (CNN) and an efficient new semi-supervised learning (SSL) framework.Two datasets were used in our experiments: Lung Nodule Analysis 16 (LUNA16), a public dataset for lung nodule classification, and a private dataset of nodule signs. The private dataset contains 641 nodules, 454 of which were annotated with 6 important signs by radiologists. Our classification system consists of 2 main parts: a 3D CNN model and an SSL method called uncertainty-aware self-training framework with consistency regularization (USC). In the system, supervised training is performed with labeled data, and simultaneously, an uncertainty-and-confidence-based strategy is used to select pseudo-labeled samples for unsupervised training, thus jointly realizing the optimization of the model.For the MLC of nodule signs, our proposed 3D CNN achieved satisfactory results with a mean average precision (mAP) of 0.870 and a mean area under the curve (AUC) of 0.782. In semi-supervised experiments, compared with supervised learning, our proposed SSL method could increase the mAP by 7.6% (from 0.730 to 0.806) and the mean AUC by 8.1% (from 0.631 to 0.712); it thus efficiently utilized the unlabeled data and achieved superior performance improvement compared to the recently advanced methods.We realized the optimal MLC of lung nodule signs with our proposed 3D CNN. Our proposed SSL method can also offer an efficient solution for the insufficiency of labeled data that may exist in the MLC tasks of 3D medical images.2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.