{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T01:25:10Z","timestamp":1755998710496,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030591366"},{"type":"electronic","value":"9783030591373"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59137-3_24","type":"book-chapter","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T12:03:04Z","timestamp":1601035384000},"page":"260-269","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A High-Throughput Tumor Location System with Deep Learning for Colorectal Cancer Histopathology Image"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7459-257X","authenticated-orcid":false,"given":"Jing","family":"Ke","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7866-3339","authenticated-orcid":false,"given":"Yiqing","family":"Shen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8050-5388","authenticated-orcid":false,"given":"Yi","family":"Guo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6390-825X","authenticated-orcid":false,"given":"Jason D.","family":"Wright","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8417-5796","authenticated-orcid":false,"given":"Naifeng","family":"Jing","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2790-5884","authenticated-orcid":false,"given":"Xiaoyao","family":"Liang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,26]]},"reference":[{"issue":"1\u20132","key":"24_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1020281327116","volume":"50","author":"C Andrieu","year":"2003","unstructured":"Andrieu, C., de Freitas, N., Doucet, A., Jordan, M.I.: An introduction to MCMC for machine learning. Mach. Learn. 50(1\u20132), 5\u201343 (2003)","journal-title":"Mach. Learn."},{"key":"24_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-030-00934-2_15","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"A BenTaieb","year":"2018","unstructured":"BenTaieb, A., Hamarneh, G.: Predicting cancer with a recurrent visual attention model for\u00a0histopathology images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 129\u2013137. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_15"},{"issue":"1","key":"24_CR3","doi-asserted-by":"publisher","first-page":"3395","DOI":"10.1038\/s41598-018-21758-3","volume":"8","author":"D Bychkov","year":"2018","unstructured":"Bychkov, D., et al.: Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep. 8(1), 3395 (2018)","journal-title":"Sci. Rep."},{"issue":"5","key":"24_CR4","doi-asserted-by":"publisher","first-page":"e0196828","DOI":"10.1371\/journal.pone.0196828","volume":"13","author":"A Cruz-Roa","year":"2018","unstructured":"Cruz-Roa, A., et al.: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection. PLoS ONE 13(5), e0196828 (2018)","journal-title":"PLoS ONE"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2424\u20132433 (2016)","DOI":"10.1109\/CVPR.2016.266"},{"key":"24_CR7","unstructured":"Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., Keutzer, K.: Densenet: implementing efficient convnet descriptor pyramids. arXiv preprint arXiv:1404.1869 (2014)"},{"issue":"3","key":"24_CR8","first-page":"270","volume":"6","author":"A Janowczyk","year":"2018","unstructured":"Janowczyk, A., Doyle, S., Gilmore, H., Madabhushi, A.: A resolution adaptive deep hierarchical (RADHical) learning scheme applied to nuclear segmentation of digital pathology images. Comput. Methods Biomech. Biomed. Eng.: Imaging Vis. 6(3), 270\u2013276 (2018)","journal-title":"Comput. Methods Biomech. Biomed. Eng.: Imaging Vis."},{"issue":"1","key":"24_CR9","doi-asserted-by":"publisher","first-page":"e1002730","DOI":"10.1371\/journal.pmed.1002730","volume":"16","author":"JN Kather","year":"2019","unstructured":"Kather, J.N., et al.: Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 16(1), e1002730 (2019)","journal-title":"PLoS Med."},{"key":"24_CR10","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1038\/s41591-019-0462-y","volume":"25","author":"JN Kather","year":"2019","unstructured":"Kather, J.N., et al.: Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054\u20131056 (2019)","journal-title":"Nat. Med."},{"key":"24_CR11","unstructured":"Li, Y., Ping, W.: Cancer metastasis detection with neural conditional random field. arXiv preprint arXiv:1806.07064 (2018)"},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"24_CR13","series-title":"Statistics and Computing","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-72634-2","volume-title":"Independent Random Sampling Methods","author":"L Martino","year":"2018","unstructured":"Martino, L., Luengo, D., M\u00edguez, J.: Independent Random Sampling Methods. SC. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-72634-2"},{"key":"24_CR14","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch (2017)"},{"issue":"11","key":"24_CR15","doi-asserted-by":"publisher","first-page":"2620","DOI":"10.1109\/TMI.2019.2907049","volume":"38","author":"T Qaiser","year":"2019","unstructured":"Qaiser, T., Rajpoot, N.M.: Learning where to see: a novel attention model for automated immunohistochemical scoring. IEEE Trans. Med. Imaging 38(11), 2620\u20132631 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"24_CR16","doi-asserted-by":"publisher","first-page":"104","DOI":"10.3322\/caac.21220","volume":"64","author":"R Siegel","year":"2014","unstructured":"Siegel, R., DeSantis, C., Jemal, A.: Colorectal cancer statistics. CA Cancer J. Clin. 64(2), 104\u2013117 (2014)","journal-title":"CA Cancer J. Clin."},{"key":"24_CR17","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Tokunaga, H., Teramoto, Y., Yoshizawa, A., Bise, R.: Adaptive weighting multi-field-of-view CNN for semantic segmentation in pathology. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12597\u201312606 (2019)","DOI":"10.1109\/CVPR.2019.01288"},{"issue":"3","key":"24_CR20","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1159\/000324734","volume":"55","author":"DC Wilbur","year":"2011","unstructured":"Wilbur, D.C.: Digital cytology: current state of the art and prospects for the future. Acta Cytol. 55(3), 227\u2013238 (2011)","journal-title":"Acta Cytol."},{"issue":"8","key":"24_CR21","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1109\/LSP.2010.2053200","volume":"17","author":"J Yan","year":"2010","unstructured":"Yan, J., Zhu, M., Liu, H., Liu, Y.: Visual saliency detection via sparsity pursuit. IEEE Signal Process. Lett. 17(8), 739\u2013742 (2010)","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59137-3_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:27:48Z","timestamp":1710340068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59137-3_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030591366","9783030591373"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59137-3_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"26 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Minneapolis, MN","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/aime20.aimedicine.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"103","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":"42","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":"1","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":"41% - 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":"3","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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}