Deep learning methods have shown outstanding classification accuracy in medical imaging problems, which is largely attributed to the availability of large-scale datasets manually annotated with clean labels. However, given the high cost of such manual annotation, new medical imaging classification problems may need to rely on machine-generated noisy labels extracted from radiology reports. Indeed, many Chest X-ray (CXR) classifiers have already been modelled from datasets with noisy labels, but their training procedure is in general not robust to noisy-label samples, leading to sub-optimal models. Furthermore, CXR datasets are mostly multi-label, so current noisy-label learning methods designed for multi-class problems cannot be easily adapted. In this paper, we propose a new method designed for the noisy multi-label CXR learning, which detects and smoothly re-labels samples from the dataset, which is then used to train common multi-label classifiers. The proposed method optimises a bag of multi-label descriptors (BoMD) to promote their similarity with the semantic descriptors produced by BERT models from the multi-label image annotation. Our experiments on diverse noisy multi-label training sets and clean testing sets show that our model has state-of-the-art accuracy and robustness in many CXR multi-label classification benchmarks.

翻译：深度学习方法在医学影像分类任务中展现出卓越的准确率，这主要得益于大规模人工标注清洁标签数据集的可用性。然而，鉴于此类人工标注的高昂成本，新型医学影像分类问题可能需要依赖从放射学报告中提取的机器生成噪声标签。事实上，许多胸部X光（CXR）分类器已在噪声标签数据集上建模，但其训练流程通常对噪声标签样本缺乏鲁棒性，导致模型性能欠佳。此外，CXR数据集多为多标签格式，因此现有面向多分类问题的噪声标签学习方法难以直接适配。本文提出一种针对噪声多标签CXR学习的新方法，该方法可检测并平滑重新标注数据集中的样本，并以此训练通用多标签分类器。所提方法优化多标签描述符集合（BoMD）以提升其与BERT模型基于多标签图像标注生成的语义描述符的相似性。我们在多种噪声多标签训练集与清洁测试集上的实验表明，该模型在多项CXR多标签分类基准测试中展现出最先进的准确率与鲁棒性。