{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T09:24:16Z","timestamp":1748683456231,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030863647"},{"type":"electronic","value":"9783030863654"}],"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\/978-3-030-86365-4_53","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T11:02:39Z","timestamp":1631271759000},"page":"660-670","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Two-Branch Neural Network for Non-Small-Cell Lung Cancer Classification and Segmentation"],"prefix":"10.1007","author":[{"given":"Borui","family":"Gao","sequence":"first","affiliation":[]},{"given":"Guangtai","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Peilin","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"53_CR1","unstructured":"American Cancer Society. https:\/\/www.cancer.org\/cancer\/lung-cancer.html"},{"issue":"4","key":"53_CR2","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/s10278-019-00182-7","volume":"32","author":"MZ Alom","year":"2019","unstructured":"Alom, M.Z., Yakopcic, C., Shamima, M.: Histopathological images with inception recurrent residual convolutional neural network. J. Digit. Imaging 32(4), 605\u2013617 (2019). https:\/\/doi.org\/10.1007\/s10278-019-00182-7","journal-title":"J. Digit. Imaging"},{"issue":"12","key":"53_CR3","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"53_CR4","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2018","unstructured":"Coudray, N., Ocampo, P.S., Sakellaropoulos, T., Narula, N., Snuderl, M.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559\u20131567 (2018). https:\/\/doi.org\/10.1038\/s41591-018-0177-5","journal-title":"Nat. Med."},{"key":"53_CR5","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"1","key":"53_CR6","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1038\/s41598-018-37638-9","volume":"9","author":"A Gertych","year":"2019","unstructured":"Gertych, A., et al.: Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci. Rep. 9(1), 1483 (2019). https:\/\/doi.org\/10.1038\/s41598-018-37638-9","journal-title":"Sci. Rep."},{"key":"53_CR7","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"53_CR8","doi-asserted-by":"publisher","first-page":"17343","DOI":"10.1038\/s41598-018-35501-5","volume":"8","author":"A Kapil","year":"2018","unstructured":"Kapil, A., et al.: Deep semi supervised generative learning for automated tumor proportion scoring on NSCLC tissue needle biopsies. Sci. Rep. 8(1), 17343 (2018). https:\/\/doi.org\/10.1038\/s41598-018-35501-5","journal-title":"Sci. Rep."},{"key":"53_CR9","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)"},{"key":"53_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1007\/978-3-030-59722-1_45","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"M Lerousseau","year":"2020","unstructured":"Lerousseau, M., et al.: Weakly supervised multiple instance learning histopathological tumor segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020, Part V. LNCS, vol. 12265, pp. 470\u2013479. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_45"},{"key":"53_CR11","doi-asserted-by":"publisher","unstructured":"Li, W., Manivannan, S., Akbar, S., Zhang, J., Trucco, E., McKenna, S.J.: Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1405\u20131408 (2016). https:\/\/doi.org\/10.1109\/ISBI.2016.7493530","DOI":"10.1109\/ISBI.2016.7493530"},{"key":"53_CR12","unstructured":"Lin, M., Chen, Q., Yan, S.: Network in network (2014)"},{"issue":"2","key":"53_CR13","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T Lin","year":"2020","unstructured":"Lin, T., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318\u2013327 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"53_CR14","doi-asserted-by":"publisher","first-page":"26286","DOI":"10.1038\/srep26286","volume":"6","author":"G Litjens","year":"2016","unstructured":"Litjens, G., S\u00e1nchez, C., Timofeeva, N., Hermsen, M.: Nagtegaal: deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6(1), 26286 (2016)","journal-title":"Sci. Rep."},{"key":"53_CR15","doi-asserted-by":"publisher","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431\u20133440 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"53_CR16","doi-asserted-by":"crossref","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey (2020)","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"53_CR17","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1007\/978-3-319-60964-5_28","volume-title":"Medical Image Understanding and Analysis","author":"T Qaiser","year":"2017","unstructured":"Qaiser, T., Tsang, Y.-W., Epstein, D., Rajpoot, N.: Tumor segmentation in whole slide images using persistent homology and deep convolutional features. In: Vald\u00e9s Hern\u00e1ndez, M., Gonz\u00e1lez-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 320\u2013329. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-60964-5_28"},{"key":"53_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"53_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/978-3-030-59722-1_49","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"H Shen","year":"2020","unstructured":"Shen, H., et al.: Deep active learning for breast cancer segmentation on immunohistochemistry images. In: Martel, A.L., et al. (eds.) MICCAI 2020, Part V. LNCS, vol. 12265, pp. 509\u2013518. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_49"},{"key":"53_CR20","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015)"},{"issue":"5","key":"53_CR21","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","volume":"35","author":"K Sirinukunwattana","year":"2016","unstructured":"Sirinukunwattana, K., Raza, S.E.A., Tsang, Y., Snead, D.R.J., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196\u20131206 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2525803","journal-title":"IEEE Trans. Med. Imaging"},{"key":"53_CR22","doi-asserted-by":"publisher","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1\u20139 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"53_CR23","doi-asserted-by":"publisher","unstructured":"Takahama, S., et al.: Multi-stage pathological image classification using semantic segmentation. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10701\u201310710 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.01080","DOI":"10.1109\/ICCV.2019.01080"},{"issue":"1","key":"53_CR24","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13000-018-0689-9","volume":"13","author":"M Udall","year":"2018","unstructured":"Udall, M., et al.: PD-L1 diagnostic tests: a systematic literature review of scoring algorithms and test-validation metrics. Diagn. Pathol. 13(1), 12 (2018)","journal-title":"Diagn. Pathol."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86365-4_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T11:16:56Z","timestamp":1631272616000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86365-4_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863647","9783030863654"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86365-4_53","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":"7 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","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":"14 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2021\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"496","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":"265","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":"4","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":"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":"2.5","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":"Conference was held online due to the COVID-19 pandemic.","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)"}}]}}