The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions.
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修剪技术已成功地用于神经网络中，以交易稀疏性。但是，网络修剪的影响并不统一：先前的工作表明，数据集中代表性不足类的召回可能会受到更大的负面影响。在这项工作中，我们通过假设模型固有的强化效应来研究回忆中的这种相对扭曲。也就是说，修剪的召回率对于以下召回精度的课程相对较差，相反，它使召回率相对较好，对于上述准确性的课程相对较好。此外，我们提出了一种旨在减弱这种效果的新修剪算法。通过统计分析，我们观察到，我们的算法的强度不那么严重，但是随着相对较困难的任务，较不复杂的模型和更高的修剪比率更为明显。更令人惊讶的是，我们相反观察到具有较低的修剪比的脱敏作用。
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