Extracting and Learning Fine-Grained Labels from Chest Radiographs.

Autor:	Syeda-Mahmood T; IBM Almaden Research Center, San Jose, California, USA., Wong KCL; IBM Almaden Research Center, San Jose, California, USA., Wu JT; IBM Almaden Research Center, San Jose, California, USA., Jadhav A; IBM Almaden Research Center, San Jose, California, USA., Boyko O; IBM Almaden Research Center, San Jose, California, USA.
Jazyk:	angličtina
Zdroj:	AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2021 Jan 25; Vol. 2020, pp. 1190-1199. Date of Electronic Publication: 2021 Jan 25 (Print Publication: 2020).
Abstrakt:	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 of457finegrained 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 offindings in images covering over nine modifiers including laterality, location, severity, size and appearance. (©2020 AMIA - All rights reserved.)
Databáze:	MEDLINE
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