Extracting and Learning Fine-Grained Labels from Chest Radiographs

• URL: http://arxiv.org/abs/2011.09517v1
• Date: Wed, 18 Nov 2020 19:56:08 GMT
• Title: Extracting and Learning Fine-Grained Labels from Chest Radiographs
• Authors: Tanveer Syeda-Mahmood, Ph.D, K.C.L Wong, Ph.D, Joy T. Wu, M.D., M.P.H, Ashutosh Jadhav, Ph.D, Orest Boyko, M.D. Ph.D
• Abstract summary: We focus on extracting and learning fine-grained labels for chest X-ray images. A total of 457 fine-grained labels depicting the largest spectrum of findings to date were selected. We show results that indicate a highly accurate label extraction process and a reliable learning of fine-grained labels.
• Score: 0.157292030677369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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