Encoder-decoder models for chest X-ray report generation perform no better than unconditioned baseli...
Encoder-decoder models for chest X-ray report generation perform no better than unconditioned baselines
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United States: Public Library of Science
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Language
English
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United States: Public Library of Science
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High quality radiology reporting of chest X-ray images is of core importance for high-quality patient diagnosis and care. Automatically generated reports can assist radiologists by reducing their workload and even may prevent errors. Machine Learning (ML) models for this task take an X-ray image as input and output a sequence of words. In this work...
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Full title
Encoder-decoder models for chest X-ray report generation perform no better than unconditioned baselines
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TN_cdi_plos_journals_2604483214
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2604483214
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ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0259639
