arXiv 2104.052612021-04-21cs.CVcs.AI

噪声标签的鲁棒分类：结合额外知识进行胸部X线摄影异常评估

Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment

Siemens Healthineers（西门子医疗）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）

Sebastian Gündel, Arnaud A. A. Setio, Florin C. Ghesu, Sasa Grbic, Bogdan Georgescu, Andreas Maier, Dorin Comaniciu

备注：: Accepted in Medical Image Analysis (MedIA). arXiv admin note: text overlap with arXiv:1905.06362

摘要：

胸部X线摄影是日常临床实践中最常见的放射学检查，用于检测各种心脏和肺部异常。需要读取和报告的数据量很大，单个放射科医生每天要进行100多项研究，这对始终保持高解释准确性构成了挑战。大规模公共数据集的引入导致了一系列用于自动异常分类的新颖系统。然而，这些数据集的标签是使用自然语言处理的医疗报告获得的，产生了很大程度的标签噪声，可能会影响性能。在这项研究中，我们提出了新的训练策略，处理来自这种次优数据的标签噪声。先前的标签概率是在由4名委员会认证的放射科医师重读的训练数据的子集上测量的，并且在训练期间用于增加训练模型对标签噪声的鲁棒性。此外，我们利用胸部X线摄影中观察到的异常的高共病率，并将此信息纳入进一步减少标签噪声的影响。此外，通过训练系统来预测肺和心脏分割以及空间知识标签，从而并入解剖学知识。为了处理来自应用不同后处理技术的各种扫描仪的多个数据集和图像，我们引入了一种新的图像归一化策略。对来自86，876名患者的297，541张胸片进行了广泛收集的实验，导致来自2个数据集的17种异常的最新性能水平。所有异常的平均AUC评分为0.880，我们提出的训练策略可用于显着提高性能评分。

英文摘要：

Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more than 100 studies per day for a single radiologist, poses a challenge in consistently maintaining high interpretation accuracy. The introduction of large-scale public datasets has led to a series of novel systems for automated abnormality classification. However, the labels of these datasets were obtained using natural language processed medical reports, yielding a large degree of label noise that can impact the performance. In this study, we propose novel training strategies that handle label noise from such suboptimal data. Prior label probabilities were measured on a subset of training data re-read by 4 board-certified radiologists and were used during training to increase the robustness of the training model to the label noise. Furthermore, we exploit the high comorbidity of abnormalities observed in chest radiography and incorporate this information to further reduce the impact of label noise. Additionally, anatomical knowledge is incorporated by training the system to predict lung and heart segmentation, as well as spatial knowledge labels. To deal with multiple datasets and images derived from various scanners that apply different post-processing techniques, we introduce a novel image normalization strategy. Experiments were performed on an extensive collection of 297,541 chest radiographs from 86,876 patients, leading to a state-of-the-art performance level for 17 abnormalities from 2 datasets. With an average AUC score of 0.880 across all abnormalities, our proposed training strategies can be used to significantly improve performance scores.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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