Chest X-ray Diagnostic Quality Assessment: How Much Is Pixel-wise Supervision Needed?

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Abstract

Chest X-ray is an important imaging method for the diagnosis of chest diseases. Chest radiograph diagnostic quality assessment is vital for the diagnosis of the disease because unqualified radiographs have negative impacts on doctors’ diagnosis and thus increase the burden on patients due to the re-acquirement of the radiographs. So far no algorithms and public data sets have been developed for chest radiograph diagnostic quality assessment. Towards effective chest X-ray diagnostic quality assessment, we analyze the image characteristics of four main chest radiograph diagnostic quality issues, i.e. Scapula Overlapping Lung, Artifact, Lung Field Loss, and Clavicle Unflatness. Our experiments show that general image classification methods are not competent for the task because the detailed information used for quality assessment by radiologists cannot be fully exploited by deep CNNs and image-level annotations. Then we propose to leverage a multi-label semantic segmentation framework to find the problematic regions, and then classify the quality issues based on the results of segmentation. However, subsequent classification is often negatively affected by certain small segmentation errors. Therefore, we propose to estimate a distance map that measures the distance from a pixel to its nearest segment, and use it to force the prediction of semantic segmentation more holistic and suitable for classification. Extensive experiments validate the effectiveness of our semantic-segmentation-based solution for chest X-ray diagnostic quality assessment. However, general segmentation-based algorithms requires fine pixel-wise annotations in the era of deep learning. In order to reduce reliance on fine annotations and further validate how important pixel-wise annotations are, weak supervision for segmentation is applied, and demonstrates its ability close to that of full supervision. Finally, we present ChestX-rayQuality, a chest radiograph data set, which comprises 480 frontal-view chest radiographs with semantic segmentation annotations and four labels of quality issue. Also, other 1212 chest radiographs with limited annotations are imported to validate our algorithms and arguments on larger data set. These two data set will be made publicly available.