Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works
Huang, S.-C., Shen, L., Lungren, M. P., & Yeung, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00391 · Cited by 489 · see citing works
Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing
Boecking, B., Usuyama, N., Bannur, S., Castro, D. C., Schwaighofer, A., Hyland, S., Wetscherek, M., Naumann, T., Nori, A., Alvarez-Valle, J., et al. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20059-5_1 · Cited by 317 · see citing works
Multi-Modal Dynamic Graph Transformer for Visual Grounding
Chen, S., & Li, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01509 · Cited by 28 · see citing works
Flagged first, then by citations
Research works printed but not linked: 18
- Z. Qin, H. Yi, Q. Lao, and K. Li, "Medical image understanding with pretrained vision language models: A comprehensive study," in proceedings of ICLR, 2023.
- J. Deng, Z. Yang, T. Chen, W. Zhou, and H. Li, "Transvg: End-to-end visual grounding with transformers," in proceedings of ICCV, 2021.
- L. Yu, Z. Lin, X. Shen, J. Yang, X. Lu, M. Bansal, and T. L. Berg, "Mattnet: Modular attention network for referring expression comprehension," in proceedings of CVPR, 2018.
- C. Zhu, Y. Zhou, Y. Shen, G. Luo, X. Pan, M. Lin, C. Chen, L. Cao, X. Sun, and R. Ji, "Seqtr: A simple yet universal network for visual grounding," in proceedings of ECCV, 2022.
- Z. Yang, B. Gong, L. Wang, W. Huang, D. Yu, and J. Luo, "A fast and accurate one-stage approach to visual grounding," in proceedings of ICCV, 2019.
- A. Kamath, M. Singh, Y. LeCun, G. Synnaeve, I. Misra, and N. Carion, "Mdetr-modulated detection for end-to-end multi-modal understanding," in proceedings of ICCV, 2021.
- A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, "An image is worth 16x16 words: Transformers for image recognition at scale," proceedings of ICLR, 2021.
- J. D. M.-W. C. Kenton and L. K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding," in proceedings of NAACL-HLT, 2019.
- R. Girshick, "Fast R-CNN," in proceedings of ICCV, 2015.
- H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese, "Generalized intersection over union: A metric and a loss for bounding box regression," in proceedings of CVPR, 2019.
- A. v. d. Oord, Y. Li, and O. Vinyals, "Representation learning with contrastive predictive coding," arXiv preprint arXiv:1807.03748, 2018. · arXiv
- M. Gutmann and A. Hyvärinen, "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models," in proceedings of International Conference on Artificial Intelligence and Statistics, 2010.
- X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, "ChestX-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," in proceedings of CVPR, 2017.
- M. Li and L. Sigal, "Referring transformer: A one-step approach to multi-task visual grounding," proceedings of NeurIPS, 2021.
- Y. Du, Z. Fu, Q. Liu, and Y. Wang, "Visual grounding with transform- ers," in proceedings of ICME, 2022.
- A. E. Johnson, T. J. Pollard, S. J. Berkowitz, N. R. Greenbaum, M. P. Lungren, C.-y. Deng, R. G. Mark, and S. Horng, "MIMIC-CXR, a de- identified publicly available database of chest radiographs with free-text reports," Scientific data, vol. 6, no. 1, p. 317, 2019.
- A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, "Pytorch: An imperative style, high- performance deep learning library," in proceedings of NeurIPS, 2019.
- I. Loshchilov and F. Hutter, "Decoupled weight decay regularization," proceedings of ICLR, 2019.
Cited by
Sorted by citations
MAIRA-2: Grounded Radiology Report Generation
Bannur, S., Bouzid, K., Castro, D. C., Schwaighofer, A., Thieme, A., Bond-Taylor, S., Ilse, M., Pérez-García, F., Salvatelli, V., Sharma, H., et al. (2024) · arXiv · doi:10.48550/arxiv.2406.04449 · Cited by 19 · see citing works
- Authors
- Chen, Z., Zhou, Y., Tran, A., Zhao, J., Wan, L., Ooi, G. S. K., Cheng, L. T.-E., Thng, C. H., Xu, X., Liu, Y., & Fu, H.
- Affiliations
- Tianjin University (CN)
- Agency for Science, Technology and Research (SG)
- Singapore General Hospital (SG)
- National Cancer Centre Singapore (SG)
- Corresponding author
- Wan, Liang
- Fu, Huazhu
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 371-381
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4387211285
- Cited by
- 26
- References
- 5
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 6 · 2023: 1
- FWCI
- 2.79
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- visual grounding, medical image analysis, radiographic diagnosis, vision-language transformers, attention mechanism, spurious correlations
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review