{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T02:08:04Z","timestamp":1776132484428,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031720826","type":"print"},{"value":"9783031720833","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-72083-3_3","type":"book-chapter","created":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T18:01:42Z","timestamp":1728842502000},"page":"25-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Clinical-Grade Multi-organ Pathology Report Generation for\u00a0Multi-scale Whole Slide Images via\u00a0a\u00a0Semantically Guided Medical Text Foundation Model"],"prefix":"10.1007","author":[{"given":"Jing Wei","family":"Tan","sequence":"first","affiliation":[]},{"given":"SeungKyu","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Eunsu","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Sung Hak","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Sangjeong","family":"Ahn","sequence":"additional","affiliation":[]},{"given":"Won-Ki","family":"Jeong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100557","volume":"24","author":"O Alfarghaly","year":"2021","unstructured":"Alfarghaly, O., Khaled, R., Elkorany, A., Helal, M., Fahmy, A.: Automated radiology report generation using conditioned transformers. Informatics in Medicine Unlocked 24, 100557 (2021)","journal-title":"Informatics in Medicine Unlocked"},{"key":"3_CR2","unstructured":"Banerjee, S., Lavie, A.: Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In: Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and\/or summarization. pp. 65\u201372 (2005)"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Chen, P., Li, H., Zhu, C., Zheng, S., Yang, L.: Mi-gen: Multiple instance generation of pathology reports for gigapixel whole-slide images (2023)","DOI":"10.1007\/978-3-031-72083-3_51"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Chen, R.J., Chen, C., Li, Y., Chen, T.Y., Trister, A.D., Krishnan, R.G., Mahmood, F.: Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 16144\u201316155 (2022)","DOI":"10.1109\/CVPR52688.2022.01567"},{"key":"3_CR6","unstructured":"Guevara, B.C., Marini, N., Marchesin, S., Aswolinskiy, W., Schlimbach, R.J., Podareanu, D., Ciompi, F.: Caption generation from histopathology whole-slide images using pre-trained transformers. In: Medical Imaging with Deep Learning, short paper track (2023)"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T.J., Zou, J.: A visual\u2013language foundation model for pathology image analysis using medical twitter. Nature medicine 29(9), 2307\u20132316 (2023)","DOI":"10.1038\/s41591-023-02504-3"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Jing, B., Xie, P., Xing, E.: On the automatic generation of medical imaging reports. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/p18-1240, http:\/\/dx.doi.org\/10.18653\/v1\/P18-1240","DOI":"10.18653\/v1\/p18-1240"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Kang, M., Song, H., Park, S., Yoo, D., Pereira, S.: Benchmarking self-supervised learning on diverse pathology datasets. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3344\u20133354 (2023)","DOI":"10.1109\/CVPR52729.2023.00326"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 14318\u201314328 (2021)","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Liu, F., Wu, X., Ge, S., Fan, W., Zou, Y.: Exploring and distilling posterior and prior knowledge for radiology report generation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 13753\u201313762 (2021)","DOI":"10.1109\/CVPR46437.2021.01354"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Lu, M.Y., Chen, B., Zhang, A., Williamson, D.F., Chen, R.J., Ding, T., Le, L.P., Chuang, Y.S., Mahmood, F.: Visual language pretrained multiple instance zero-shot transfer for histopathology images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 19764\u201319775 (2023)","DOI":"10.1109\/CVPR52729.2023.01893"},{"key":"3_CR13","unstructured":"Luke, M.K., Sebastian, G., Rush\u00a0Alexander, M., et\u00a0al.: Encoder-agnostic adaptation for conditional language generation. arXiv (2019)"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Nguyen, A.T., Kwak, J.T.: Gpc: Generative and general pathology image classifier. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 203\u2013212. Springer (2023)","DOI":"10.1007\/978-3-031-47401-9_20"},{"key":"3_CR15","unstructured":"Papanikolaou, Y., Pierleoni, A.: Dare: Data augmented relation extraction with gpt-2 (2020)"},{"key":"3_CR16","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.: A method for automatic evaluation of machine translation\u201d. the Proceedings of ACL-2002, ACL, Philadelphia, PA, July 2002 (2001)"},{"key":"3_CR17","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019)"},{"key":"3_CR18","unstructured":"ROUGE, L.C.: A package for automatic evaluation of summaries. In: Proceedings of Workshop on Text Summarization of ACL, Spain. vol.\u00a05 (2004)"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Sengupta, S., Brown, D.E.: Automatic report generation for histopathology images using pre-trained vision transformers. arXiv preprint arXiv:2311.06176 (2023)","DOI":"10.1109\/ISBI56570.2024.10635175"},{"key":"3_CR20","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Tan, J.W., Nguyen, K.T., Lee, K., Jeong, W.K.: Multi-scale contrastive learning with attention for histopathology image classification. In: Medical Imaging 2023: Digital and Computational Pathology. vol. 12471, pp. 294\u2013301. SPIE (2023)","DOI":"10.1117\/12.2653423"},{"key":"3_CR22","doi-asserted-by":"publisher","unstructured":"Tanida, T., M\u00fcller, P., Kaissis, G., Rueckert, D.: Interactive and explainable region-guided radiology report generation. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (Jun 2023).https:\/\/doi.org\/10.1109\/cvpr52729.2023.00718, http:\/\/dx.doi.org\/10.1109\/CVPR52729.2023.00718","DOI":"10.1109\/cvpr52729.2023.00718"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Thandiackal, K., Chen, B., Pati, P., Jaume, G., Williamson, D.F., Gabrani, M., Goksel, O.: Differentiable zooming for multiple instance learning on whole-slide images. In: European Conference on Computer Vision. pp. 699\u2013715. Springer (2022)","DOI":"10.1007\/978-3-031-19803-8_41"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"You, D., Liu, F., Ge, S., Xie, X., Zhang, J., Wu, X.: Aligntransformer: Hierarchical alignment of visual regions and disease tags for medical report generation (2022)","DOI":"10.1007\/978-3-030-87199-4_7"},{"key":"3_CR25","unstructured":"Zhang, R., Weber, C., Grossman, R., Khan, A.A.: Evaluating and interpreting caption prediction for histopathology images. In: Machine Learning for Healthcare Conference. pp. 418\u2013435. PMLR (2020)"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Gao, J., Zhou, M., Wang, X., Qiao, Y., Zhang, S., Wang, D.: Text-guided foundation model adaptation for pathological image classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 272\u2013282. Springer (2023)","DOI":"10.1007\/978-3-031-43904-9_27"},{"issue":"5","key":"3_CR27","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/s42256-019-0052-1","volume":"1","author":"Z Zhang","year":"2019","unstructured":"Zhang, Z., Chen, P., McGough, M., Xing, F., Wang, C., Bui, M., Xie, Y., Sapkota, M., Cui, L., Dhillon, J., et\u00a0al.: Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nature Machine Intelligence 1(5), 236\u2013245 (2019)","journal-title":"Nature Machine Intelligence"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72083-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T15:36:24Z","timestamp":1732894584000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72083-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031720826","9783031720833"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72083-3_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"14 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"There are no conflicts of interest to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}