The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Deep learning in generating radiology reports: A survey
Open accessAbstract
Highlights • Deep Learning algorithms showed promising results in generating radiology reports.• We categorize state of the art models into three levels: word, sentence and paragraph.• We review the publicly available datasets of radiology images and linked reports.• We compare results of the generated reports through quantitative evaluation matrices.• Researchers integrate convolutional neural network and recurrent neural network.
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Cited by
Transformers in medical imaging: A survey
Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102802 · Cited by 1,296
Advances in medical image analysis with vision Transformers: A comprehensive review
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Evaluating progress in automatic chest X-ray radiology report generation
Yu, F., Endo, M., Krishnan, R., Pan, I., Tsai, A., Reis, E. P., Fonseca, E. K. U. N., Lee, H. M. H., Abad, Z. S. H., Ng, A. Y., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100802 · Cited by 200
“There are three main categories: the first generates free text based on semantics extracted from input chest X-ray images 20,33,34 ; the second retrieves existing text that best matches input images from a report corpus 17,35 ; and the third selects curated templates corresponding to a predefined…”
Metadata
- Authors
- Monshi, M. M. A., Poon, J., & Chung, V.
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 106
- Pages
- 101878
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W3024545783
- Cited by
- 192
- References
- 33
- FWCI
- 10.03
- Type
- journal article
- Language
- en
- Open access
- green
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