{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T10:14:39Z","timestamp":1726136079889},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030872366"},{"type":"electronic","value":"9783030872373"}],"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\/978-3-030-87237-3_22","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T02:19:41Z","timestamp":1632363581000},"page":"227-237","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Hierarchical Graph Pathomic Network for Progression Free Survival Prediction"],"prefix":"10.1007","author":[{"given":"Zichen","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jiayun","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhufeng","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Wenyuan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Anthony","family":"Sisk","sequence":"additional","affiliation":[]},{"given":"Huihui","family":"Ye","sequence":"additional","affiliation":[]},{"given":"William","family":"Speier","sequence":"additional","affiliation":[]},{"given":"Corey W.","family":"Arnold","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2019. CA Cancer J. Clin. 69(1), 7\u201334 (2019)","DOI":"10.3322\/caac.21551"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Epstein, J.I., et al.: A contemporary prostate cancer grading system: a validated alternative to the Gleason score. Euro. Urol. 69(3), 428\u2013435 (2016)","DOI":"10.1016\/j.eururo.2015.06.046"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Chandramouli, S., et al.: Computer extracted features from initial H&E tissue biopsies predict disease progression for prostate cancer patients on active surveillance. Cancers 12(9), 2708 (2020)","DOI":"10.3390\/cancers12092708"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Leo, P., et al.: Computerized histomorphometric features of glandular architecture predict risk of biochemical recurrence following radical prostatectomy: a multisite study (2019)","DOI":"10.1200\/JCO.2019.37.15_suppl.5060"},{"issue":"11","key":"22_CR5","doi-asserted-by":"publisher","first-page":"1438","DOI":"10.1038\/s41374-018-0095-7","volume":"98","author":"L Cheng","year":"2018","unstructured":"Cheng, L., et al.: Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers. Lab. Invest. 98(11), 1438\u20131448 (2018)","journal-title":"Lab. Invest."},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Zhao, Y., et al.: Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4837\u20134846 (2020)","DOI":"10.1109\/CVPR42600.2020.00489"},{"key":"22_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-030-00934-2_20","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"R Li","year":"2018","unstructured":"Li, R., Yao, J., Zhu, X., Li, Y., Huang, J.: Graph CNN for survival analysis on whole slide pathological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 174\u2013182. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_20"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Adnan, M., Kalra, S., Tizhoosh, H.R.: Representation learning of histopathology images using graph neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 988\u2013989 (2020)","DOI":"10.1109\/CVPRW50498.2020.00502"},{"key":"22_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-030-59713-9_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"K Ding","year":"2020","unstructured":"Ding, K., Liu, Q., Lee, E., Zhou, M., Lu, A., Zhang, S.: Feature-enhanced graph networks for genetic mutational prediction using histopathological images in colon cancer. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 294\u2013304. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_29"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Graham, S., Koohbanani, N.A., Shaban, M., Heng, P.-H., Rajpoot, N.: CGC-net: cell graph convolutional network for grading of colorectal cancer histology images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00050"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, R.J., Lu, M.Y., Baras, A., Mahmood, F.: Weakly supervised prostate TMA classification via graph convolutional networks. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 239\u2013243. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098534"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans. Med. Imaging (2020)","DOI":"10.1109\/TMI.2020.3021387"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: A multi-resolution model for histopathology image classification and localization with multiple instance learning. In: Computers in Biology and Medicine, p. 104253 (2021)","DOI":"10.1016\/j.compbiomed.2021.104253"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Kumar, N., et al.: A multi-organ nucleus segmentation challenge. IEEE Trans. Med. Imaging 39(5), 1380\u20131391 (2019)","DOI":"10.1109\/TMI.2019.2947628"},{"key":"22_CR16","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"22_CR17","unstructured":"Lee, J., Lee, I., Kang, J.: Self-attention graph pooling. In: International Conference on Machine Learning, pp. 3734\u20133743. PMLR (2019)"},{"key":"22_CR18","unstructured":"Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)"},{"key":"22_CR19","unstructured":"Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: Deep survival: a deep cox proportional hazards network. Stat 1050(2) (2016)"},{"key":"22_CR20","unstructured":"Hu, W., et al.: Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265 (2019)"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Qiu, J., et al.: GCC: graph contrastive coding for graph neural network pre-training. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1150\u20131160 (2020)","DOI":"10.1145\/3394486.3403168"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"22_CR23","unstructured":"Ing, N., et al.: Semantic segmentation for prostate cancer grading by convolutional neural networks. In: Medical Imaging 2018: Digital Pathology, vol. 10581, pp. 105811B. International Society for Optics and Photonics (2018)"},{"key":"22_CR24","doi-asserted-by":"crossref","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 (2018)","DOI":"10.1016\/j.cell.2018.02.052"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Van Griethuysen, J.J.M., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104\u2013e107 (2017)","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"22_CR26","unstructured":"Davidson-Pilon, C., et al.: Camdavidsonpilon\/lifelines: v0. 24.15. Zenodo (2020)"}],"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\/978-3-030-87237-3_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T03:02:57Z","timestamp":1632366177000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87237-3_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872366","9783030872373"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87237-3_22","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)"}}]}}