{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T02:54:41Z","timestamp":1764557681803,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031395383"},{"type":"electronic","value":"9783031395390"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-39539-0_1","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T00:02:38Z","timestamp":1690675358000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Do Tissue Source Sites Leave Identifiable Signatures in\u00a0Whole Slide Images Beyond Staining?"],"prefix":"10.1007","author":[{"given":"Piotr","family":"Keller","sequence":"first","affiliation":[]},{"given":"Muhammad","family":"Dawood","sequence":"additional","affiliation":[]},{"given":"Fayyaz ul Amir","family":"Minhas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","unstructured":"Liu, J., et al.: An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173(2), 400\u2013416.e11 (2018). https:\/\/doi.org\/10.1016\/j.cell.2018.02.052","DOI":"10.1016\/j.cell.2018.02.052"},{"issue":"3","key":"1_CR2","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1002\/path.5331","volume":"249","author":"E Abels","year":"2019","unstructured":"Abels, E., et al.: Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the digital pathology association. J. Pathol. 249(3), 286\u2013294 (2019)","journal-title":"J. Pathol."},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: 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"},{"issue":"8","key":"1_CR4","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1016\/j.ccell.2022.07.004","volume":"40","author":"RJ Chen","year":"2022","unstructured":"Chen, R.J., et al.: Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell 40(8), 865\u2013878 (2022)","journal-title":"Cancer Cell"},{"issue":"10","key":"1_CR5","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2018","unstructured":"Coudray, N., et al.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559\u20131567 (2018)","journal-title":"Nat. Med."},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Dawood, M., Branson, K., Rajpoot, N.M., Minhas, F.: Albrt: cellular composition prediction in routine histology images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 664\u2013673 (2021)","DOI":"10.1109\/ICCVW54120.2021.00080"},{"key":"1_CR7","doi-asserted-by":"publisher","unstructured":"Dawood, M., Branson, K., Rajpoot, N.M., Minhas, F.U.A.A.: All you need is color: image based spatial gene expression prediction using neural stain learning. In: Kamp, M., et al. (eds.) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. CCIS, vol. 1525, pp. 437\u2013450. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-93733-1_32","DOI":"10.1007\/978-3-030-93733-1_32"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Fischer, A.H., Jacobson, K.A., Rose, J., Zeller, R.: Hematoxylin and eosin staining of tissue and cell sections. Cold Spring Harb. Protoc. 2008(5), pdb-prot4986 (2008)","DOI":"10.1101\/pdb.prot4986"},{"issue":"12","key":"1_CR9","doi-asserted-by":"publisher","first-page":"3312","DOI":"10.1093\/bioinformatics\/btac315","volume":"38","author":"A Foote","year":"2022","unstructured":"Foote, A., Asif, A., Rajpoot, N., Minhas, F.: REET: robustness evaluation and enhancement toolbox for computational pathology. Bioinformatics 38(12), 3312\u20133314 (2022)","journal-title":"Bioinformatics"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Graham, S., et al.: Lizard: a large-scale dataset for colonic nuclear instance segmentation and classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 684\u2013693 (2021)","DOI":"10.1109\/ICCVW54120.2021.00082"},{"issue":"2","key":"1_CR11","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.cell.2018.03.022","volume":"173","author":"KA Hoadley","year":"2018","unstructured":"Hoadley, K.A., et al.: Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173(2), 291\u2013304 (2018)","journal-title":"Cell"},{"issue":"1","key":"1_CR12","doi-asserted-by":"publisher","first-page":"4423","DOI":"10.1038\/s41467-021-24698-1","volume":"12","author":"FM Howard","year":"2021","unstructured":"Howard, F.M., et al.: The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat. Commun. 12(1), 4423 (2021)","journal-title":"Nat. Commun."},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Keller, P., Dawood, M., Minhas, F.U.A.A.: Maximum mean discrepancy kernels for predictive and prognostic modeling of whole slide images. arXiv preprint arXiv:1111.6285 (2023)","DOI":"10.1109\/ISBI53787.2023.10230578"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Keller, P., Dawood, M., et al.: Maximum mean discrepancy kernels for predictive and prognostic modeling of whole slide images. arXiv preprint arXiv:2301.09624 (2023)","DOI":"10.1109\/ISBI53787.2023.10230578"},{"issue":"6","key":"1_CR15","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1109\/TBME.2014.2303294","volume":"61","author":"AM Khan","year":"2014","unstructured":"Khan, A.M., Rajpoot, N., Treanor, D., Magee, D.: A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans. Biomed. Eng. 61(6), 1729\u20131738 (2014)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"6","key":"1_CR16","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. Nat. Biomed. Eng. 5(6), 555\u2013570 (2021)","journal-title":"Nat. Biomed. Eng."},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Lu, W., Toss, M., Dawood, M., Rakha, E., Rajpoot, N., Minhas, F.: Slidegraph+: whole slide image level graphs to predict her2 status in breast cancer. Med. Image Anal. 80, 102486 (2022)","DOI":"10.1016\/j.media.2022.102486"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1107\u20131110. IEEE (2009)","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"1_CR19","unstructured":"Mackenzie, C.C., Dawood, M., Graham, S., Eastwood, M., ul Amir Afsar Minhas, F.: Neural graph modelling of whole slide images for survival ranking. In: Rieck, B., Pascanu, R. (eds.) Proceedings of the First Learning on Graphs Conference. Proceedings of Machine Learning Research, vol. 198, pp. 48:1\u201348:10. PMLR, 09\u201312 December 2022"},{"issue":"5","key":"1_CR20","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. IEEE Comput. Graph. Appl. 21(5), 34\u201341 (2001)","journal-title":"IEEE Comput. Graph. Appl."},{"key":"1_CR21","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1007\/11941439_114","volume-title":"AI 2006: Advances in Artificial Intelligence","author":"M Sokolova","year":"2006","unstructured":"Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-Score and ROC: a family of discriminant measures for performance evaluation. In: Sattar, A., Kang, B. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1015\u20131021. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11941439_114"},{"key":"1_CR22","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. Med. Image Anal. 65, 101789 (2020)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Trustworthy Machine Learning for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39539-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T03:03:31Z","timestamp":1702868611000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39539-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031395383","9783031395390"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39539-0_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TML4H","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Trustworthy Machine Learning for Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"tml4h2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cse.hkust.edu.hk\/~jhc\/2023tml4h.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.73","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.44","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}