{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T07:30:51Z","timestamp":1769844651439,"version":"3.49.0"},"publisher-location":"Cham","reference-count":21,"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_20","type":"book-chapter","created":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T18:01:42Z","timestamp":1728842502000},"page":"211-221","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["IHCSurv: Effective Immunohistochemistry Priors for\u00a0Cancer Survival Analysis in\u00a0Gigapixel Multi-stain Whole Slide Images"],"prefix":"10.1007","author":[{"given":"Yejia","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanqing","family":"Chao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongwei","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixuan","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nishchal","family":"Sapkota","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danny Z.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dakai","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Bian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"issue":"1","key":"20_CR1","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/s12938-023-01157-0","volume":"22","author":"CC Atabansi","year":"2023","unstructured":"Atabansi, C.C., Nie, J., Liu, H., Song, Q., Yan, L., Zhou, X.: A survey of Transformer applications for histopathological image analysis: New developments and future directions. BioMedical Engineering OnLine 22(1), \u00a096 (2023)","journal-title":"BioMedical Engineering OnLine"},{"issue":"1","key":"20_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-17204-5","volume":"7","author":"P Bankhead","year":"2017","unstructured":"Bankhead, P., Loughrey, M.B., Fern\u00e1ndez, J.A., Dombrowski, Y., McArt, D.G., Dunne, P.D., McQuaid, S., Gray, R.T., Murray, L.J., Coleman, H.G., et\u00a0al.: QuPath: Open source software for digital pathology image analysis. Scientific Reports 7(1), \u00a01\u20137 (2017)","journal-title":"Scientific Reports"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Bug, D., Feuerhake, F., Merhof, D.: Foreground extraction for histopathological whole slide imaging. In: Bildverarbeitung f\u00fcr die Medizin 2015: Algorithmen-Systeme-Anwendungen. pp. 419\u2013424. Springer (2015)","DOI":"10.1007\/978-3-662-46224-9_72"},{"key":"20_CR4","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: CVPR. pp. 16144\u201316155 (2022)","DOI":"10.1109\/CVPR52688.2022.01567"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Chen, R.J., Lu, M.Y., Shaban, M., Chen, C., Chen, T.Y., Williamson, D.F., Mahmood, F.: Whole slide images are 2D point clouds: Context-aware survival prediction using patch-based graph convolutional networks. In: MICCAI. pp. 339\u2013349. Springer (2021)","DOI":"10.1007\/978-3-030-87237-3_33"},{"key":"20_CR6","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1109\/TIP.2021.3139229","volume":"31","author":"D Di","year":"2022","unstructured":"Di, D., Zhang, J., Lei, F., Tian, Q., Gao, Y.: Big-hypergraph factorization neural network for survival prediction from whole slide image. IEEE Transactions on Image Processing 31, 1149\u20131160 (2022)","journal-title":"IEEE Transactions on Image Processing"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Dwivedi, C., Nofallah, S., Pouryahya, M., Iyer, J., Leidal, K., et\u00a0al.: Multi stain graph fusion for multimodal integration in pathology. In: CVPR. vol.\u00a02021, pp. 1835\u20131845 (2021)","DOI":"10.1109\/CVPRW56347.2022.00200"},{"issue":"2","key":"20_CR8","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1038\/s41591-022-02134-1","volume":"29","author":"S Foersch","year":"2023","unstructured":"Foersch, S., Glasner, C., Woerl, A.C., Eckstein, M., Wagner, D.C., Schulz, S., Kellers, F., Fernandez, A., Tserea, K., Kloth, M., et\u00a0al.: Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nature Medicine 29(2), 430\u2013439 (2023)","journal-title":"Nature Medicine"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Huang, Z., Chai, H., Wang, R., Wang, H., Yang, Y., Wu, H.: Integration of patch features through self-supervised learning and Transformer for survival analysis on whole slide images. In: MICCAI. pp. 561\u2013570. Springer (2021)","DOI":"10.1007\/978-3-030-87237-3_54"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Li, R., Yao, J., Zhu, X., Li, Y., Huang, J.: Graph CNN for survival analysis on whole slide pathological images. In: MICCAI. pp. 174\u2013182. Springer (2018)","DOI":"10.1007\/978-3-030-00934-2_20"},{"issue":"6","key":"20_CR11","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nature Biomedical Engineering 5(6), 555\u2013570 (2021)","journal-title":"Nature Biomedical Engineering"},{"issue":"23","key":"20_CR12","doi-asserted-by":"publisher","first-page":"4359","DOI":"10.1158\/0008-5472.CAN-22-1190","volume":"82","author":"H Mi","year":"2022","unstructured":"Mi, H., Sivagnanam, S., Betts, C.B., Liudahl, S.M., Jaffee, E.M., Coussens, L.M., Popel, A.S.: Quantitative spatial profiling of immune populations in pancreatic ductal adenocarcinoma reveals tumor microenvironment heterogeneity and prognostic biomarkers. Cancer Research 82(23), 4359\u20134372 (2022)","journal-title":"Cancer Research"},{"key":"20_CR13","unstructured":"Muhammad, H., Xie, C., Sigel, C.S., Doukas, M., Alpert, L., Simpson, A.L., Fuchs, T.J.: EPIC-survival: End-to-end part inferred clustering for survival analysis, with prognostic stratification boosting. In: Medical Imaging with Deep Learning (2021)"},{"issue":"5","key":"20_CR14","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/38.946629","volume":"21","author":"E Reinhard","year":"2001","unstructured":"Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. Computer Graphics and Applications 21(5), 34\u201341 (2001)","journal-title":"Computer Graphics and Applications"},{"key":"20_CR15","first-page":"2136","volume":"34","author":"Z Shao","year":"2021","unstructured":"Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., et\u00a0al.: TransMIL: Transformer based correlated multiple instance learning for whole slide image classification. NeurIPS 34, 2136\u20132147 (2021)","journal-title":"NeurIPS"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Shao, Z., Chen, Y., Bian, H., Zhang, J., Liu, G., Zhang, Y.: HVTSurv: Hierarchical vision Transformer for patient-level survival prediction from whole slide image. In: AAAI. vol.\u00a037, pp. 2209\u20132217 (2023)","DOI":"10.1609\/aaai.v37i2.25315"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Wood, R., Domingo, E., Sirinukunwattana, K., Lafarge, M.W., Koelzer, V.H., Maughan, T.S., Rittscher, J.: Joint prediction of response to therapy, molecular traits, and spatial organisation in colorectal cancer biopsies. In: MICCAI. pp. 758\u2013767. Springer (2023)","DOI":"10.1007\/978-3-031-43904-9_73"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Yan, R., Lv, Z., Yang, Z., Lin, S., Zheng, C., Zhang, F.: Sparse and hierarchical Transformer for survival analysis on whole slide images. IEEE Journal of Biomedical and Health Informatics (2023)","DOI":"10.1109\/JBHI.2023.3307584"},{"key":"20_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101789","volume":"65","author":"J Yao","year":"2020","unstructured":"Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N., Huang, J.: Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Medical Image Analysis 65, 101789 (2020)","journal-title":"Medical Image Analysis"},{"issue":"9","key":"20_CR20","doi-asserted-by":"publisher","first-page":"3126","DOI":"10.1109\/TPAMI.2020.2979450","volume":"43","author":"SG Zadeh","year":"2020","unstructured":"Zadeh, S.G., Schmid, M.: Bias in cross-entropy-based training of deep survival networks. TPAMI 43(9), 3126\u20133137 (2020)","journal-title":"TPAMI"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Zhu, X., Yao, J., Huang, J.: Deep convolutional neural network for survival analysis with pathological images. In: BIBM. pp. 544\u2013547. IEEE (2016)","DOI":"10.1109\/BIBM.2016.7822579"}],"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_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T18:05:55Z","timestamp":1728842755000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72083-3_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031720826","9783031720833"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72083-3_20","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":"The authors have no competing interests to declare that are relevant to the content of this article.","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"}}]}}