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