{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:11:11Z","timestamp":1757311871202,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030872397"},{"type":"electronic","value":"9783030872403"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87240-3_72","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T07:44:03Z","timestamp":1632383043000},"page":"752-761","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["AMINN: Autoencoder-Based Multiple Instance Neural Network Improves Outcome Prediction in Multifocal Liver Metastases"],"prefix":"10.1007","author":[{"given":"Jianan","family":"Chen","sequence":"first","affiliation":[]},{"given":"Helen M. C.","family":"Cheung","sequence":"additional","affiliation":[]},{"given":"Laurent","family":"Milot","sequence":"additional","affiliation":[]},{"given":"Anne L.","family":"Martel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"issue":"4","key":"72_CR1","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1109\/MSP.2019.2900993","volume":"36","author":"P Afshar","year":"2019","unstructured":"Afshar, P., Mohammadi, A., Plataniotis, K.N., Oikonomou, A., Benali, H.: From handcrafted to deep-learning-based cancer radiomics: challenges and opportunities. IEEE Signal Process. Mag. 36(4), 132\u2013160 (2019)","journal-title":"IEEE Signal Process. Mag."},{"key":"72_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1007\/978-3-030-32251-9_63","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Chen","year":"2019","unstructured":"Chen, J., Milot, L., Cheung, H.M.C., Martel, A.L.: Unsupervised clustering of quantitative imaging phenotypes using autoencoder and gaussian mixture model. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 575\u2013582. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_63"},{"key":"72_CR3","doi-asserted-by":"crossref","unstructured":"Cheung, H.M., et al.: Late gadolinium enhancement of colorectal liver metastases post-chemotherapy is associated with tumour fibrosis and overall survival post-hepatectomy. European radiology, pp. 1\u20138 (2018)","DOI":"10.1007\/s00330-018-5331-4"},{"issue":"3","key":"72_CR4","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1097\/01.sla.0000138076.72547.b1","volume":"240","author":"FG Fernandez","year":"2004","unstructured":"Fernandez, F.G., Drebin, J.A., Linehan, D.C., Dehdashti, F., Siegel, B.A., Strasberg, S.M.: Five-year survival after resection of hepatic metastases from colorectal cancer in patients screened by positron emission tomography with f-18 fluorodeoxyglucose (fdg-pet). Ann. Surg. 240(3), 438 (2004)","journal-title":"Ann. Surg."},{"issue":"3","key":"72_CR5","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.clcc.2011.03.023","volume":"10","author":"R Ferrarotto","year":"2011","unstructured":"Ferrarotto, R., et al.: Durable complete responses in metastatic colorectal cancer treated with chemotherapy alone. Clin. Colorectal Cancer 10(3), 178\u2013182 (2011)","journal-title":"Clin. Colorectal Cancer"},{"issue":"3","key":"72_CR6","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1097\/00000658-199909000-00004","volume":"230","author":"Y Fong","year":"1999","unstructured":"Fong, Y., Fortner, J., Sun, R.L., Brennan, M.F., Blumgart, L.H.: Clinical score for predicting recurrence after hepatic resection for metastatic colorectal cancer: analysis of 1001 consecutive cases. Ann. Surg. 230(3), 309 (1999)","journal-title":"Ann. Surg."},{"issue":"2","key":"72_CR7","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies, R.J., Kinahan, P.E., Hricak, H.: Radiomics: images are more than pictures, they are data. Radiology 278(2), 563\u2013577 (2016)","journal-title":"Radiology"},{"key":"72_CR8","unstructured":"Ilse, M., Tomczak, J.M., Welling, M.: Attention-based deep multiple instance learning. In: International Conference in Machine Learning (2018)"},{"issue":"2","key":"72_CR9","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"72_CR10","unstructured":"Maron, O., Lozano-P\u00e9rez, T.: A framework for multiple-instance learning. In: Advances in Neural Information Processing Systems, pp. 570\u2013576 (1998)"},{"key":"72_CR11","doi-asserted-by":"crossref","unstructured":"Nakai, Y., et al.: Mri findings of liver parenchyma peripheral to colorectal liver metastasis: A potential predictor of long-term prognosis. Radiology, p. 202367 (2020)","DOI":"10.1148\/radiol.2020202367"},{"key":"72_CR12","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1109\/RBME.2017.2651164","volume":"10","author":"G Quellec","year":"2017","unstructured":"Quellec, G., Cazuguel, G., Cochener, B., Lamard, M.: Multiple-instance learning for medical image and video analysis. IEEE Rev. Biomed. Eng. 10, 213\u2013234 (2017)","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"7","key":"72_CR13","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1002\/bjs.9471","volume":"101","author":"K Roberts","year":"2014","unstructured":"Roberts, K., et al.: Performance of prognostic scores in predicting long-term outcome following resection of colorectal liver metastases. Br. J. Surg. 101(7), 856\u2013866 (2014)","journal-title":"Br. J. Surg."},{"issue":"1","key":"72_CR14","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1097\/SLA.0000000000002064","volume":"267","author":"K Sasaki","year":"2018","unstructured":"Sasaki, K., et al.: The tumor burden score: a new \u201cmetro-ticket\u2019\u2019 prognostic tool for colorectal liver metastases based on tumor size and number of tumors. Ann. Surg. 267(1), 132\u2013141 (2018)","journal-title":"Ann. Surg."},{"issue":"2","key":"72_CR15","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1061\/(ASCE)0887-3801(2000)14:2(109)","volume":"14","author":"JJ Shi","year":"2000","unstructured":"Shi, J.J.: Reducing prediction error by transforming input data for neural networks. J. Comput. Civ. Eng. 14(2), 109\u2013116 (2000)","journal-title":"J. Comput. Civ. Eng."},{"key":"72_CR16","doi-asserted-by":"crossref","unstructured":"Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics. CA: Aancer J. Clinicians 69(1), 7\u201334 (2019)","DOI":"10.3322\/caac.21551"},{"issue":"1","key":"72_CR17","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc.: Ser. B (Methodol.) 58(1), 267\u2013288 (1996)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"issue":"2","key":"72_CR18","doi-asserted-by":"publisher","first-page":"166","DOI":"10.5005\/jp-journals-10018-1241","volume":"7","author":"AI Valderrama-Trevi\u00f1o","year":"2017","unstructured":"Valderrama-Trevi\u00f1o, A.I., Barrera-Mera, B., Ceballos-Villalva, J.C., Montalvo-Jav\u00e9, E.E.: Hepatic metastasis from colorectal cancer. Euroasian J. Hepato-Gastroenterology 7(2), 166 (2017)","journal-title":"Euroasian J. Hepato-Gastroenterology"},{"issue":"21","key":"72_CR19","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJ Van Griethuysen","year":"2017","unstructured":"Van Griethuysen, J.J., et al.: Computational radiomics system to decode the radiographic phenotype. Can. Res. 77(21), e104\u2013e107 (2017)","journal-title":"Can. Res."},{"key":"72_CR20","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.patcog.2017.08.026","volume":"74","author":"X Wang","year":"2018","unstructured":"Wang, X., Yan, Y., Tang, P., Bai, X., Liu, W.: Revisiting multiple instance neural networks. Pattern Recogn. 74, 15\u201324 (2018)","journal-title":"Pattern Recogn."},{"key":"72_CR21","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.radonc.2018.10.027","volume":"130","author":"ML Welch","year":"2019","unstructured":"Welch, M.L., et al.: Vulnerabilities of radiomic signature development: the need for safeguards. Radiother. Oncol. 130, 2\u20139 (2019)","journal-title":"Radiother. Oncol."},{"issue":"13","key":"72_CR22","doi-asserted-by":"publisher","first-page":"R150","DOI":"10.1088\/0031-9155\/61\/13\/R150","volume":"61","author":"SS Yip","year":"2016","unstructured":"Yip, S.S., Aerts, H.J.: Applications and limitations of radiomics. Phys. Med. Biol. 61(13), R150 (2016)","journal-title":"Phys. Med. Biol."},{"key":"72_CR23","unstructured":"Zhou, S.K., Rueckert, D., Fichtinger, G.: Handbook of medical image computing and computer assisted intervention. Academic Press (2019)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87240-3_72","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T18:51:53Z","timestamp":1725821513000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87240-3_72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872397","9783030872403"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87240-3_72","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/en\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}