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In this work we present a method for the detection of radiological findings,their location and differential diagnoses from chest x-rays. Unlike prior worksthat focus on the detection of few pathologies, we use a hierarchical taxonomymapped to the Unified Medical Language System (UMLS) terminology to identify189 radiological findings, 22 differential diagnosis and 122 anatomiclocations, including ground glass opacities, infiltrates, consolidations andother radiological findings compatible with COVID-19. We train the system onone large database of 92,594 frontal chest x-rays (AP or PA, standing, supineor decubitus) and a second database of 2,065 frontal images of COVID-19patients identified by at least one positive Polymerase Chain Reaction (PCR)test. The reference labels are obtained through natural language processing ofthe radiological reports. On 23,159 test images, the proposed neural networkobtains an AUC of 0.94 for the diagnosis of COVID-19. To our knowledge, thiswork uses the largest chest x-ray dataset of COVID-19 positive cases to dateand is the first one to use a hierarchical labeling schema and to provideinterpretability of the results, not only by using network attention methods,but also by indicating the radiological findings that have led to thediagnosis.

UMLS-ChestNet: A deep convolutional neural network for radiological findings, differential diagnoses and localizations of COVID-19 in chest x-rays