Covid Radiographic Images Data-set for A.I

Overview

The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the Artificial intelligence community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non- COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.

COVID-19 virus has rapidly spread in mainland China and into multiple countries worldwide. As of April 7th 2020 in Italy, one of the most severely affected countries, 135,586 Patients with COVID19 were recorded, and 17,127 of them died; at the time of writing Piedmont is the 3rd most affected region in Italy, with 13,343 recorded cases. Early diagnosis is a key element for proper treatment of the patients and prevention of the spread of the disease. Given the high tropism of COVID-19 for respiratory airways and lung epithelium, identification of lung involvement in infected patients can be relevant for treatment and monitoring of the disease. Virus testing is currently considered the only specific method of diagnosis. The Center for Disease Control (CDC) in the US recommends collecting and testing specimens from the upper respiratory tract (nasopharyngeal and oropharyngeal swabs) or from the lower respiratory tract when available (bronchoalveolar lavage, BAL) for viral testing with reverse transcription polymerase chain reaction (RT-PCR) assay. Current position papers from radiological societies (Fleischner Society, SIRM, RSNA) do not recommend routine use of imaging for COVID-19 diagnosis. However, it has been widely demonstrated that, even at early stages of the disease, chest x-rays (CXR) and computed tomography (CT) scans can show pathological findings. It should be noted that they are actually non specific, and overlap with other viral infections (such as influenza, H1N1, SARS and MERS): most authors report peripheral bilateral ill-defined and ground-glass opacities, mainly involving the lower lobes, progressively increasing in extension as disease becomes more severe and leading to diffuse parenchymal consolidation, CT is a sensitive tool for early detection of peripheral ground glass opacities; however routine role of CT imaging in these Patients is logistically challenging in terms of safety for health professionals and other patients, and can overwhelm available resources. Chest X-ray can be a useful tool, especially in emergency settings: it can help exclude other possible lung "noxa", allow a first rough valuation of the extent of lung involvement and most importantly can be obtained at patients bed using portable devices, limiting possible exposure in health care workers and other patients. Furthermore, CXR can be repeated over time to monitor the evolution of lung disease. Methodology: we describe the deeplearning approach based on quite standard pipeline, namely chest image pre-processing and lung segmentation followed by classification model obtained with transfer learning. As we will see in this section, data pre-processing is fundamental to remove any bias present in the data. In particular, we will show that it is easy for a deep model to recognize these biases which drive the learning process. Given the small size of COVID datasets, a key role is played by the larger datasets used for pre-training. Therefore, we first discuss which datasets can be used for our goals.