The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Clinically Correct Report Generation from Chest X-Rays Using Templates
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The application of AI to image interpretation tasks has often been limited to the identification of a handful of individual pathologies, representing an over-simplification of the image interpretation task.
“However, the application of AI to image interpretation tasks has often been limited to the identification of a handful of individual pathologies,8,9,10 representing an over-simplification of the image interpretation task.”
From Evaluating progress in automatic chest X-ray radiology report generation (2023) · cites this paper as [10]
Citations
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Generating Radiology Reports via Memory-driven Transformer
Chen, Z., Song, Y., Chang, T.-H., & Wan, X. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.112 · Cited by 693
On the Automatic Generation of Medical Imaging Reports
Jing, B., Xie, P., & Xing, E. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1240 · Cited by 657 · cited 5×
“Most works use common Convolutional Neural Networks (CNNs) as encoder (e.g. Densenet [6]), and as decoder: a single LSTM to generate word by word [2]; two LSTMs arranged hierarchically to generate sentences and words [9,10,16,31]; or a Transformer-based network [3,17,29].”
BackgroundMethodResultInfluentialA Structured Review of the Validity of BLEU
Reiter, E. (2018) · Computational Linguistics · doi:10.1162/coli_a_00322 · Cited by 386
MIMIC-CXR-JPG - chest radiographs with structured labels
Johnson, A., Lungren, M., Peng, Y., Lu, Z., Mark, R., Berkowitz, S., & Horng, S. (2024) · PhysioNet · doi:10.13026/8360-t248 · Cited by 46
Flagged first, then by citations
Research works printed but not linked: 23
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- Boag, W., Hsu, T.M.H., Mcdermott, M., Berner, G., Alesentzer, E., Szolovits, P.: Baselines for chest X-ray report generation. In: ML4H at NeurIPS (2020)
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Cited by
Sorted by citations
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
Deep learning for report generation on chest X-ray images
Ouis, M. Y., & A. Akhloufi, M. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102320 · Cited by 25
Metadata
- Authors
- Pino, P., Parra, D., Besa, C., & Lagos, C.
- Affiliations
- Pontificia Universidad Católica de Chile (CL)
- Corresponding author
- Pino, Pablo
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 654-663
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3204650824
- Cited by
- 21
- References
- 5
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 6 · 2023: 6 · 2022: 4 · 2021: 1
- FWCI
- 1.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- chest X-ray report generation, anomaly detection, verbalization, CheXpert, MIMIC-CXR
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