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问题中展现出卓越的分类准确性，这主要归功于可用的大规模人工标注干净标签数据集。然而，由于此类人工标注成本高昂，新的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）分类器已基于噪声标签数据集建模，但其训练过程通常对噪声标签样本缺乏鲁棒性，导致模型性能次优。此外，CXR数据集大多为多标签问题，因此当前针对多类问题设计的噪声标签学习方法难以直接适配。本文提出一种专用于噪声多标签CXR学习的新方法，该方法检测并平滑地重新标注数据集中的样本，随后用于训练常见的多标签分类器。所提方法优化多标签描述子包（BoMD），以提升其与BERT模型从多标签图像标注中生成的语义描述子的相似性。在多样化的噪声多标签训练集和干净测试集上的实验表明，我们的模型在多个CXR多标签分类基准测试中达到了最先进的准确性和鲁棒性。