{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:52:31Z","timestamp":1759333951355,"version":"build-2065373602"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"29","license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s00521-024-10640-1","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T03:51:23Z","timestamp":1733457083000},"page":"23789-23797","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Contrastive concept-phrase pre-training for generating clinically accurate and interpretable chest X-ray reports"],"prefix":"10.1007","volume":"37","author":[{"given":"Abdallah","family":"Tubaishat","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8176-3373","authenticated-orcid":false,"given":"Tehseen","family":"Zia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Windridge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Nawaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saad","family":"Razzaq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"10640_CR1","doi-asserted-by":"crossref","unstructured":"Johnson AE, Pollard TJ, Greenbaum NR, Lungren MP, Deng C-y, Peng Y, Lu Z, Mark RG, Berkowitz SJ, Horng S (2019) Mimic-cxr-jpg, a large publicly available database of labeled chest radiographs, arXiv preprint arXiv:1901.07042","DOI":"10.1038\/s41597-019-0322-0"},{"key":"10640_CR2","doi-asserted-by":"crossref","unstructured":"Chen Z, Song Y, Chang T-H, Wan X (2020) Generating radiology reports via memory-driven transformer, arXiv preprint arXiv:2010.16056","DOI":"10.18653\/v1\/2020.emnlp-main.112"},{"key":"10640_CR3","doi-asserted-by":"crossref","unstructured":"Wang X, Peng Y, Lu L, Lu Z, Summers RM (2018) Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\u00a09049\u20139058","DOI":"10.1109\/CVPR.2018.00943"},{"key":"10640_CR4","doi-asserted-by":"crossref","unstructured":"Yuan J, Liao H, Luo R, Luo J (2019) Automatic radiology report generation based on multi-view image fusion and medical concept enrichment, In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part VI 22, pp.\u00a0721\u2013729, Springer","DOI":"10.1007\/978-3-030-32226-7_80"},{"key":"10640_CR5","doi-asserted-by":"crossref","unstructured":"Jing B, Xie P, Xing E (2017) On the automatic generation of medical imaging reports, arXiv preprint arXiv:1711.08195","DOI":"10.18653\/v1\/P18-1240"},{"key":"10640_CR6","doi-asserted-by":"crossref","unstructured":"Miura Y, Zhang Y, Tsai EB, Langlotz CP, Jurafsky D (2020) Improving factual completeness and consistency of image-to-text radiology report generation, arXiv preprint arXiv:2010.10042","DOI":"10.18653\/v1\/2021.naacl-main.416"},{"key":"10640_CR7","unstructured":"Endo M, Krishnan R, Krishna V, Ng AY, Rajpurkar P (2021) Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model, In: Machine Learning for Health, pp.\u00a0209\u2013219, PMLR"},{"key":"10640_CR8","unstructured":"Liu G, Hsu T-MH, McDermott M, Boag W, Weng W-H, Szolovits P, Ghassemi M (2019) Clinically accurate chest x-ray report generation, In: Machine Learning for Healthcare Conference, pp.\u00a0249\u2013269, PMLR"},{"key":"10640_CR9","unstructured":"Gale W, Oakden-Rayner L, Carneiro G, Bradley AP, Palmer LJ (2018) Producing radiologist-quality reports for interpretable artificial intelligence, arXiv preprint arXiv:1806.00340"},{"key":"10640_CR10","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.patrec.2022.02.005","volume":"156","author":"T Zia","year":"2022","unstructured":"Zia T, Murtaza S, Bashir N, Windridge D, Nisar Z (2022) Vant-Gan: adversarial learning for discrepancy-based visual attribution in medical imaging. Pattern Recogn Lett 156:112\u2013118","journal-title":"Pattern Recogn Lett"},{"key":"10640_CR11","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J et\u00a0al., (2021) Learning transferable visual models from natural language supervision, In: International conference on machine learning, pp.\u00a08748\u20138763, PMLR"},{"key":"10640_CR12","doi-asserted-by":"crossref","unstructured":"Azizi S, Mustafa B, Ryan F, Beaver Z, Freyberg J, Deaton J, Loh A, Karthikesalingam A, Kornblith S, Chen T et\u00a0al., (2021) Big self-supervised models advance medical image classification, In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp.\u00a03478\u20133488","DOI":"10.1109\/ICCV48922.2021.00346"},{"key":"10640_CR13","first-page":"10486","volume":"34","author":"B Cao","year":"2020","unstructured":"Cao B, Zhang H, Wang N, Gao X, Shen D (2020) Auto-Gan: self-supervised collaborative learning for medical image synthesis. Proceed AAAI Conf Artif Intell 34:10486\u201310493","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"10640_CR14","doi-asserted-by":"crossref","unstructured":"Li LH, Zhang P, Zhang H, Yang J, Li C, Zhong Y, Wang L, Yuan L, Zhang L, Hwang J-N et\u00a0al., (2022) Grounded language-image pre-training, In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.\u00a010965\u201310975","DOI":"10.1109\/CVPR52688.2022.01069"},{"key":"10640_CR15","doi-asserted-by":"crossref","unstructured":"Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM (2017) Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\u00a02097\u20132106","DOI":"10.1109\/CVPR.2017.369"},{"key":"10640_CR16","doi-asserted-by":"crossref","unstructured":"Wu T-W, Huang J-H, Lin J, Worring M (2023) Expert-defined keywords improve interpretability of retinal image captioning, In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp.\u00a01859\u20131868","DOI":"10.1109\/WACV56688.2023.00190"},{"key":"10640_CR17","doi-asserted-by":"crossref","unstructured":"Huang J-H, Yang C-HH, Liu F, Tian M, Liu Y-C, Wu T-W, Lin I, Wang K, Morikawa H, Chang H et\u00a0al., (2021) Deepopht: medical report generation for retinal images via deep models and visual explanation, In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp.\u00a02442\u20132452","DOI":"10.1109\/WACV48630.2021.00249"},{"key":"10640_CR18","doi-asserted-by":"crossref","unstructured":"Schlegl T, Waldstein SM, Vogl W-D, Schmidt-Erfurth U, Langs G (2015) Predicting semantic descriptions from medical images with convolutional neural networks, In: Information Processing in Medical Imaging: 24th International Conference, IPMI 2015, Sabhal Mor Ostaig, Isle of Skye, UK, June 28-July 3, 2015, Proceedings, pp.\u00a0437\u2013448, Springer","DOI":"10.1007\/978-3-319-19992-4_34"},{"key":"10640_CR19","doi-asserted-by":"crossref","unstructured":"Wu T-W, Huang J-H, Lin J, Worring M (2023) Expert-defined keywords improve interpretability of retinal image captioning, In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp.\u00a01859\u20131868","DOI":"10.1109\/WACV56688.2023.00190"},{"key":"10640_CR20","doi-asserted-by":"crossref","unstructured":"Schlegl T, Waldstein SM, Vogl W-D, Schmidt-Erfurth U, Langs G (2015) Predicting semantic descriptions from medical images with convolutional neural networks, In: Information Processing in Medical Imaging: 24th International Conference, IPMI 2015, Sabhal Mor Ostaig, Isle of Skye, UK, June 28-July 3, 2015, Proceedings, pp.\u00a0437\u2013448, Springer","DOI":"10.1007\/978-3-319-19992-4_34"},{"key":"10640_CR21","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\u00a02921\u20132929","DOI":"10.1109\/CVPR.2016.319"},{"key":"10640_CR22","first-page":"590","volume":"33","author":"J Irvin","year":"2019","unstructured":"Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, Marklund H, Haghgoo B, Ball R, Shpanskaya K et al (2019) Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Proceed AAAI Conf Artif Intell 33:590\u2013597","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"10640_CR23","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805"},{"key":"10640_CR24","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized bert pretraining approach, arXiv preprint arXiv:1907.11692"},{"key":"10640_CR25","unstructured":"Jia C, Yang Y, Xia Y, Chen Y-T, Parekh Z, Pham H, Le Q, Sung Y-H, Li Z, Duerig T (2021) Scaling up visual and vision-language representation learning with noisy text supervision, In: International Conference on Machine Learning, pp.\u00a04904\u20134916, PMLR"},{"key":"10640_CR26","first-page":"2982","volume":"36","author":"B Yan","year":"2022","unstructured":"Yan B, Pei M (2022) Clinical-Bert: vision-language pre-training for radiograph diagnosis and reports generation. Proceed AAAI Conf Artif Intell 36:2982\u20132990","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"10640_CR27","unstructured":"Zhang Y, Jiang H, Miura Y, Manning CD, Langlotz CP (2022) Contrastive learning of medical visual representations from paired images and text, In: Machine Learning for Healthcare Conference, pp.\u00a02\u201325, PMLR"},{"key":"10640_CR28","doi-asserted-by":"crossref","unstructured":"Cornia M, Stefanini M, Baraldi L, Cucchiara R (2020) Meshed-memory transformer for image captioning, In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp.\u00a010578\u201310587","DOI":"10.1109\/CVPR42600.2020.01059"},{"key":"10640_CR29","doi-asserted-by":"crossref","unstructured":"Nooralahzadeh F, Gonzalez NP, Frauenfelder T, Fujimoto K, Krauthammer M (2021) Progressive transformer-based generation of radiology reports, arXiv preprint arXiv:2102.09777","DOI":"10.18653\/v1\/2021.findings-emnlp.241"},{"key":"10640_CR30","unstructured":"Li Y, Liang X, Hu Z, Xing EP (2018) Hybrid retrieval-generation reinforced agent for medical image report generation. Adv Neural Inf Process Syst 31"},{"key":"10640_CR31","first-page":"12910","volume":"34","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Wang X, Xu Z, Yu Q, Yuille A, Xu D (2020) When radiology report generation meets knowledge graph. Proceed AAAI Conf Artif Intell 34:12910\u201312917","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"10640_CR32","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\u00a0770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10640_CR33","doi-asserted-by":"crossref","unstructured":"Alsentzer E, Murphy JR, Boag W, Weng W-H, Jin D, Naumann T, McDermott M (2019) Publicly available clinical bert embeddings, arXiv preprint arXiv:1904.03323","DOI":"10.18653\/v1\/W19-1909"},{"key":"10640_CR34","doi-asserted-by":"crossref","unstructured":"Cho J, Yoon S, Kale A, Dernoncourt F, Bui T, Bansal M (2022) Fine-grained image captioning with clip reward, arXiv preprint arXiv:2205.13115","DOI":"10.18653\/v1\/2022.findings-naacl.39"},{"key":"10640_CR35","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877\u20131901","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"10640_CR36","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1038\/s41597-019-0322-0","volume":"6","author":"AE Johnson","year":"2019","unstructured":"Johnson AE, Pollard TJ, Berkowitz SJ, Greenbaum NR, Lungren MP, Deng C-Y, Mark RG, Horng S (2019) Mimic-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data 6(1):317","journal-title":"Sci Data"},{"key":"10640_CR37","unstructured":"Boag W, Hsu T-MH, McDermott M, Berner G, Alesentzer E, Szolovits P (2020) Baselines for chest x-ray report generation, In: Machine learning for health workshop, pp.\u00a0126\u2013140, PMLR"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10640-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10640-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10640-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T05:22:40Z","timestamp":1759209760000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10640-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,6]]},"references-count":37,"journal-issue":{"issue":"29","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["10640"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10640-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,12,6]]},"assertion":[{"value":"9 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}