Extracting and Learning Fine-Grained Labels from Chest Radiographs. (arXiv:2011.09517v1 [cs.CV])


Chest radiographs are the most common diagnostic exam in emergency rooms and
intensive care units today. Recently, a number of researchers have begun
working on large chest X-ray datasets to develop deep learning models for
recognition of a handful of coarse finding classes such as opacities, masses
and nodules. In this paper, we focus on extracting and learning fine-grained
labels for chest X-ray images. Specifically we develop a new method of
extracting fine-grained labels from radiology reports by combining
vocabulary-driven concept extraction with phrasal grouping in dependency parse
trees for association of modifiers with findings. A total of 457 fine-grained
labels depicting the largest spectrum of findings to date were selected and
sufficiently large datasets acquired to train a new deep learning model
designed for fine-grained classification. We show results that indicate a
highly accurate label extraction process and a reliable learning of
fine-grained labels. The resulting network, to our knowledge, is the first to
recognize fine-grained descriptions of findings in images covering over nine
modifiers including laterality, location, severity, size and appearance.

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