Extracting and Learning Fine-Grained Labels from Chest Radiographs
| Autor: | Syeda-Mahmood, Tanveer, D, Ph., Wong, K. C. L, Wu, Joy T., D., M., H, M. P., Jadhav, Ashutosh, Boyko, Orest, D, M. D. Ph. |
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| Rok vydání: | 2020 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: This paper won the Homer R. Warner Award at AMIA 2020 awarded to a paper that best describes approaches to improving computerized information acquisition, knowledge data acquisition and management, and experimental results documenting the value of these approaches. The paper shows a combination of textual and visual processing to automatically recognize complex findings in chest X-rays |
| Databáze: | arXiv |
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