The DOI matches its Crossref record; 50 of the 51 references it cites were found in a registry or an index.
METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens
Open accessAbstract
In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature. To this end, we propose METransformer, a method to realize this idea with a transformer-based backbone. The key design of our method is the introduction of multiple learnable “expert” tokens into both the transformer encoder and decoder. In the encoder, each expert token interacts with both vision tokens and other expert tokens to learn to attend different image regions for image representation. These expert tokens are encouraged to capture complementary information by an orthogonal loss that minimizes their over-lap. In the decoder, each attended expert token guides the cross-attention between input words and visual tokens, thus influencing the generated report. A metrics-based expert voting strategy is further developed to generate the final report. By the multi-experts concept, our model enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts. Experimental results demonstrate the promising performance of our proposed model on two widely used benchmarks. Last but not least, the framework-level innovation makes our work ready to incorporate advances on existing “single-expert” models to further improve its performance.
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“Such references can be removed to reduce hallucinations when prior studies are not available, 9,14,18 or used in conjunction with prior images to enable descriptions of change.17,19,20”
From MAIRA-2: Grounded Radiology Report Generation (2024) · cites this paper as [14]
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Cites
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325 · cited 14×
“Specifically, we compare METransformer with 5 state-of-the-art (SOTA) image captioning methods, including Show-tell [32], AdaAtt [24], Att2in [1], Transformer [7] and M2transformer [6].”
MethodInfluentialAn Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472 · cited 3×
“1, our encoder comprises a vision transformer (ViT) [9] encoder and an expert bilinear transformer encoder.”
MethodInfluentialSqueeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667 · cited 1×
“A squeeze-excitation operation [10] to B mid is performed to obtain channel-wise attention Bilinear Encoder Layer.”
MethodPyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · cited 1×
“We implement our model using Pytorch [27] and Pytorch-lightning library 2 with two NVIDIA GeForce RTX 3090 GPU cards.”
MethodLayer NormalizationPreprint
Ba, J., Kiros, J., & Hinton, G. E. (2016) · arXiv.org · doi:10.48550/arxiv.1607.06450 · Cited by 13,762 · cited 2×
“Layer-norm(LN) [2] is applied before every block and residual connections are applied after every block.”
BackgroundMethod(2004) · Cited by 9,718
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · cited 4×
“As shown in Table 1, on both datasets, our METransformer consistently outperforms those “single-expert” based models, including attention-based baselines (Att2in [37], AdaAtt [24]), memory-augmented meth-”
BackgroundMethodInfluentialShow and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318 · cited 4×
“A broad collection of methods was proposed in the last few years [1,6,24,25,28,32,37,42,45,48] and have achieved great success in advancing the stateof-the-art.”
BackgroundMethodInfluentialBottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S. J., & Zhang, L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00636 · Cited by 5,416 · cited 7×
“Specifically, we compare METransformer with 5 state-of-the-art (SOTA) image captioning methods, including Show-tell [32], AdaAtt [24], Att2in [1], Transformer [7] and M2transformer [6].”
BackgroundMethodInfluentialCIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845
(2005) · Cited by 4,152
Bleu: a method for automatic evaluation of machine translation
Papineni, K. (2024) · doi:10.57702/ljteqmrb · Cited by 3,668
Bilinear CNN Models for Fine-Grained Visual Recognition
Lin, T., RoyChowdhury, A., & Maji, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.170 · Cited by 2,234 · cited 2×
“Further, in the bilinear transformer encoder, to enable each “expert” to capture fine-grained image information, we compute higher-order attention between expert tokens and visual tokens, which has proved to be effective in fine-grained classification tasks [20].”
BackgroundMethodSelf-Critical Sequence Training for Image Captioning
Rennie, S. J., Marcheret, E., Mroueh, Y., Ross, J., & Goel, V. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.131 · Cited by 2,231
Image Captioning with Semantic Attention
You, Q., Jin, H., Wang, Z., Chen, F., & Luo, J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.503 · Cited by 1,869 · cited 1×
“A broad collection of methods was proposed in the last few years [1,6,24,25,28,32,37,42,45,48] and have achieved great success in advancing the stateof-the-art.”
BackgroundSelf-citationOn the Variance of the Adaptive Learning Rate and BeyondPreprint
Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., & Han, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03265 · Cited by 1,837 · cited 1×
“Our model is trained using Radam optimizer [23] with a mini-batch size of 16.”
MethodSelf-citationKnowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning
Lu, J., Xiong, C., Parikh, D., & Socher, R. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.345 · Cited by 1,724 · cited 5×
“Specifically, we compare METransformer with 5 state-of-the-art (SOTA) image captioning methods, including Show-tell [32], AdaAtt [24], Att2in [1], Transformer [7] and M2transformer [6].”
BackgroundMethodInfluentialMeshed-Memory Transformer for Image Captioning
Cornia, M., Stefanini, M., Baraldi, L., & Cucchiara, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01059 · Cited by 1,259 · cited 2×
“A broad collection of methods was proposed in the last few years [1,6,24,25,28,32,37,42,45,48] and have achieved great success in advancing the stateof-the-art.”
BackgroundPreparing a collection of radiology examinations for distribution and retrieval
Demner-Fushman, D., Kohli, M. D., Rosenman, M. B., Shooshan, S. E., Rodriguez, L., Antani, S., Thoma, G. R., & McDonald, C. J. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv080 · Cited by 1,164 · cited 2×
“In this experiment, two datasets are used for the performance evaluation, i.e., a widely-used benchmark IU-Xray [8] and the currently largest dataset MIMIC-CXR [13] for medical report generation.”
BackgroundMethodVL-BERT: Pre-training of Generic Visual-Linguistic Representations
Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., & Dai, J. (2019) · arXiv · doi:10.48550/arxiv.1908.08530 · Cited by 950 · cited 1×
“We introduce two types of the segment, “[0]” and “[1]”, to separate the input tokens from different sources [29], i.”
BackgroundX-Linear Attention Networks for Image Captioning
Pan, Y., Yao, T., Li, Y., & Mei, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01098 · Cited by 720 · cited 4×
“Most of them adopt the conventional encoder-decoder architecture, with convolutional neural networks (CNNs) as the encoder and recurrent (e.g., LSTM/GRU) or non-recurrent networks (e.g., Transformer) as the decoder with a carefully designed attention module [24, 25, 37] to produce image description.”
BackgroundMethodInfluentialHadamard Product for Low-rank Bilinear PoolingPreprint
Kim, J.-H., On, K.-W., Lim, W., Kim, J., Ha, J.-W., & Zhang, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.04325 · Cited by 702
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 8×
“Moreover, eight SOTA medical report generation models are involved in the comparison, including CoAtt [12], HGRNAgent [17], R2Gen [5], R2GenCMN [4], PPKED [21], GSK [41] and MSAT [34].”
BackgroundMethodResultInfluentialExploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation
Liu, F., Wu, X., Ge, S., Fan, W., & Zou, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01354 · Cited by 422 · cited 6×
“Moreover, eight SOTA medical report generation models are involved in the comparison, including CoAtt [12], HGRNAgent [17], R2Gen [5], R2GenCMN [4], PPKED [21], GSK [41] and MSAT [34].”
BackgroundMethodInfluentialCheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301590 · Cited by 392 · cited 1×
“For this purpose, the CheXpert [11] is applied to labeling the generated reports and the results are compared with ground truths in 14 different categories related to thoracic diseases and support devices.”
MethodCross-modal Memory Networks for Radiology Report Generation
Chen, Z., Shen, Y., Song, Y., & Wan, X. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.459 · Cited by 369
When Radiology Report Generation Meets Knowledge Graph
Zhang, Y., Wang, X., Xu, Z., Yu, Q., Yuille, A., & Xu, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6989 · Cited by 348 · cited 2×
“Most recently, many works [16, 21, 22, 40, 41, 47] attempt to integrate knowledge graphs into medical report generation pipeline to improve the quality of the generated reports.”
BackgroundKnowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation
Li, C. Y., Liang, X., Hu, Z., & Xing, E. P. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016666 · Cited by 316 · cited 2×
“[12] and HGRG-Agent [17] on IU-Xray are quoted from [15] while the other results are quoted from their respective papers.”
BackgroundHybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation
Li, C. Y., Liang, X., Hu, Z., & Xing, E. P. (2018) · arXiv · doi:10.48550/arxiv.1805.08298 · Cited by 271 · cited 4×
“CoAtt [12] and HGRNAgent [17] only report results on IU-Xray in their original paper.”
BackgroundMethodInfluentialMultimodal Recurrent Model with Attention for Automated Radiology Report Generation
Xue, Y., Xu, T., Long, L. R., Xue, Z., Antani, S., Thoma, G. R., & Huang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_52 · Cited by 245
Review Networks for Caption GenerationPreprint
Yang, Z., Yuan, Y., Wu, Y., Salakhutdinov, R., & Cohen, W. W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.07912 · Cited by 230
Knowledge matters: Chest radiology report generation with general and specific knowledge
Yang, S., Wu, X., Ge, S., Zhou, S. K., & Xiao, L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102510 · Cited by 225
Yuan, J., Liao, H., Luo, R., & Luo, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_80 · Cited by 215 · cited 1×
“For example, some works [12,35,38,39,43, 46] utilize a hierarchically structured LSTM network to better handle the long narrative nature of medical reports.”
BackgroundRadiology report generation with a learned knowledge base and multi-modal alignment
Yang, S., Wu, X., Ge, S., Zheng, Z., Zhou, S. K., & Xiao, L. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102798 · Cited by 199 · cited 1×
“Most recently, many works [16, 21, 22, 40, 41, 47] attempt to integrate knowledge graphs into medical report generation pipeline to improve the quality of the generated reports.”
BackgroundMIMIC-CXR: A large publicly available database of labeled chest radiographsPreprint
Johnson, A. E. W., Pollard, T., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Mark, R. G., & Horng, S. (2019) · arXiv (Cornell University) · Cited by 182
You, D., Liu, F., Ge, S., Xie, X., Zhang, J., & Wu, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_7 · Cited by 176 · cited 1×
“Another group of works [34, 44] utilizes disease tags to facilitate report generation.”
BackgroundAuxiliary signal-guided knowledge encoder-decoder for medical report generation
Li, M., Liu, R., Wang, F., Chang, X., & Liang, X. (2022) · World Wide Web · doi:10.1007/s11280-022-01013-6 · Cited by 155 · cited 2×
“Due to the fine-grained nature of radiology images, it is hard to focus on the correct image regions throughout the report generation procedure despite different attentions developed in recent works [16, 21].”
BackgroundMore Grounded Image Captioning by Distilling Image-Text Matching Model
Zhou, Y., Wang, M., Liu, D., Hu, Z., & Zhang, H. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00483 · Cited by 147
Automated Radiographic Report Generation Purely on Transformer: A Multicriteria Supervised Approach
Wang, Z., Han, H., Wang, L., Li, X., & Zhou, L. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3171661 · Cited by 118 · cited 1×
“To further improve performance, some works [4, 5, 33] employ a transformer instead of LSTM as the report decoder, which has achieved good results.”
MethodSelf-citationAutomatic Generation of Medical Imaging Diagnostic Report with Hierarchical Recurrent Neural Network
Yin, C., Qian, B., Wei, J., Li, X., Zhang, X., Li, Y., & Zheng, Q. (2019) · Proceedings · doi:10.1109/icdm.2019.00083 · Cited by 116 · cited 1×
“For example, some works [12,35,38,39,43, 46] utilize a hierarchically structured LSTM network to better handle the long narrative nature of medical reports.”
BackgroundA Self-boosting Framework for Automated Radiographic Report Generation
Wang, Z., Zhou, L., Wang, L., & Li, X. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00246 · Cited by 95 · cited 2×
“In addition, Wang et al [35] introduced an image-report matching network to bridge the domain gap between image and text for reducing the difficulty of report generation.”
BackgroundSelf-citationA Medical Semantic-Assisted Transformer for Radiographic Report Generation
Wang, Z., Tang, M., Wang, L., Li, X., & Zhou, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_63 · Cited by 81
Self-citationAuto-Encoding Knowledge Graph for Unsupervised Medical Report GenerationPreprint
Liu, F., You, C., Wu, X., Ge, S., Wang, S., & Sun, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.04318 · Cited by 81 · cited 2×
“To measure the accuracy of descriptions for clinical abnormalities, we follow [4, 5, 22] and further report clinical efficacy metrics.”
BackgroundMethodImproved Disease Classification in Chest X-Rays with Transferred Features from Report Generation
Xue, Y., & Huang, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_10 · Cited by 33 · cited 2×
“For example, some works [12,35,38,39,43, 46] utilize a hierarchically structured LSTM network to better handle the long narrative nature of medical reports.”
BackgroundLi, Y., Pan, Y., Chen, J., Yao, T., & Mei, T. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3478331 · Cited by 28
Research works printed but not linked: 1
- su, 2019, Vl-bert: Pre-training of generic visuallinguistic representations, ArXiv Preprint
Cited by
Sorted by citations
R2GenGPT: Radiology Report Generation with frozen LLMs
Wang, Z., Liu, L., Wang, L., & Zhou, L. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100033 · Cited by 253
Self-citationExplainable Deep Learning Methods in Medical Image Classification: A Survey
Patrício, C., Neves, J. C., & Teixeira, L. F. (2023) · ACM Computing Surveys · doi:10.1145/3625287 · Cited by 207
“Table 3 conveys the results in terms of a set of NLP evaluation metrics (BLEU score, ROUGE, METEOR, and CIDEr) for each of the selected methods.”
BackgroundPromptMRG: Diagnosis-Driven Prompts for Medical Report Generation
Jin, H., Che, H., Lin, Y., & Chen, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.28038 · Cited by 181
Automated Radiology Report Generation: A Review of Recent Advances
Sloan, P., Clatworthy, P. L., Simpson, E., & Mirmehdi, M. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3408456 · Cited by 106
“Chen et al. [51]’s method has been utilised to assess an ARRG models performance by various researchers [11], [34], [49], [50], [67], [103], [123], [129].”
InfluentialVision-Language Models in medical image analysis: From simple fusion to general large models
Li, X., Li, L., Jiang, Y., Wang, H., Qiao, X., Feng, T., Luo, H., & Zhao, Y. J. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102995 · Cited by 93
Metadata
- Authors
- Wang, Z., Liu, L., Wang, L., & Zhou, L.
- Affiliations
- The University of Sydney (AU)
- The University of Adelaide (AU)
- University of Wollongong (AU)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 11558-11567
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076536
- Semantic Scholar
- 4d90382730eefbe8639e64f6855f206657758cc7
- Cited by
- 184
- References
- 51
- Citations by year
- 2026: 58 · 2025: 82 · 2024: 37 · 2023: 7
- FWCI
- 19.62
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- Research Online (University of Wollongong) (submittedVersion)
- arXiv (Cornell University)
- 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
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- radiology report generation, vision-language transformers, cross-attention
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