{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T06:45:46Z","timestamp":1751093146264,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031471964"},{"type":"electronic","value":"9783031449178"}],"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\/978-3-031-44917-8_6","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T20:33:48Z","timestamp":1696710828000},"page":"60-70","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multitask Framework for\u00a0Label Refinement and\u00a0Lesion Segmentation in\u00a0Clinical Brain Imaging"],"prefix":"10.1007","author":[{"given":"Yang","family":"Yu","sequence":"first","affiliation":[]},{"given":"Jiahao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ashish Jith","family":"Sreejith Kumar","sequence":"additional","affiliation":[]},{"given":"Bryan","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Navya","family":"Vanjavaka","sequence":"additional","affiliation":[]},{"given":"Nurul Hafidzah","family":"Rahim","sequence":"additional","affiliation":[]},{"given":"Alistair","family":"Koh","sequence":"additional","affiliation":[]},{"given":"Shaheen","family":"Low","sequence":"additional","affiliation":[]},{"given":"Yih Yian","family":"Sitoh","sequence":"additional","affiliation":[]},{"given":"Hanry","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Pavitra","family":"Krishnaswamy","sequence":"additional","affiliation":[]},{"given":"Ivan","family":"Ho Mien","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","volume":"30","author":"Z Akkus","year":"2017","unstructured":"Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digital Imaging 30, 449\u2013459 (2017)","journal-title":"J. Digital Imaging"},{"key":"6_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-030-32245-8_11","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Bertels","year":"2019","unstructured":"Bertels, J., et al.: Optimizing the dice score and jaccard index for medical image segmentation: theory and practice. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 92\u2013100. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_11"},{"key":"6_CR3","doi-asserted-by":"publisher","unstructured":"Cai, Z., Xin, J., Shi, P., Zhou, S., Wu, J., Zheng, N.: Meta pixel loss correction for medical image segmentation with noisy labels. In: Medical Image Learning with Limited and Noisy Data: First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. pp. 32\u201341. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16760-7_4","DOI":"10.1007\/978-3-031-16760-7_4"},{"key":"6_CR4","unstructured":"Cheng, G., Ji, H., Tian, Y.: Walking on two legs: learning image segmentation with noisy labels. In: Conference on Uncertainty in Artificial Intelligence, pp. 330\u2013339. PMLR (2020)"},{"key":"6_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1007\/978-3-030-87196-3_43","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"R Guo","year":"2021","unstructured":"Guo, R., Pagnucco, M., Song, Y.: Learning with noise: mask-guided attention model for weakly supervised nuclei segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 461\u2013470. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_43"},{"key":"6_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity Mappings in Deep Residual Networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"issue":"9","key":"6_CR7","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/34.232073","volume":"15","author":"DP Huttenlocher","year":"1993","unstructured":"Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850\u2013863 (1993)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101759","volume":"65","author":"D Karimi","year":"2020","unstructured":"Karimi, D., Dou, H., Warfield, S.K., Gholipour, A.: Deep learning with noisy labels: exploring techniques and remedies in medical image analysis. Med. Image Anal. 65, 101759 (2020)","journal-title":"Med. Image Anal."},{"key":"6_CR9","unstructured":"Kim, J., Kim, M., Kang, H., Lee, K.: U-gat-it: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation. arXiv preprint arXiv:1907.10830 (2019)"},{"key":"6_CR10","unstructured":"Lee, D.H., et al.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, ICML. p. 896 (2013)"},{"issue":"1","key":"6_CR11","first-page":"8672","volume":"12","author":"J Lee","year":"2022","unstructured":"Lee, J., et al.: A pixel-level coarse-to-fine image segmentation labelling algorithm. Sci. Reports 12(1), 8672 (2022)","journal-title":"Sci. Reports"},{"key":"6_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-030-87193-2_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"S Li","year":"2021","unstructured":"Li, S., Gao, Z., He, X.: Superpixel-Guided Iterative Learning from Noisy Labels for Medical Image Segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 525\u2013535. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_50"},{"issue":"10","key":"6_CR13","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-030-11726-9_28","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"A Myronenko","year":"2019","unstructured":"Myronenko, A.: 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311\u2013320. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_28"},{"key":"6_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-020-00505-z","volume":"20","author":"R Obuchowicz","year":"2020","unstructured":"Obuchowicz, R., Oszust, M., Piorkowski, A.: Interobserver variability in quality assessment of magnetic resonance images. BMC Med. Imaging 20, 1\u201310 (2020)","journal-title":"BMC Med. Imaging"},{"issue":"11","key":"6_CR16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002686","volume":"15","author":"P Rajpurkar","year":"2018","unstructured":"Rajpurkar, P., et al.: Deep learning for chest radiograph diagnosis: a retrospective comparison of the chexnext algorithm to practicing radiologists. PLoS Med. 15(11), e1002686 (2018)","journal-title":"PLoS Med."},{"issue":"1","key":"6_CR17","first-page":"1","volume":"11","author":"R Ranjbarzadeh","year":"2021","unstructured":"Ranjbarzadeh, R., Bagherian Kasgari, A., Jafarzadeh Ghoushchi, S., Anari, S., Naseri, M., Bendechache, M.: Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci. Reports 11(1), 1\u201317 (2021)","journal-title":"Sci. Reports"},{"key":"6_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. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"7","key":"6_CR19","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/TMI.2004.828354","volume":"23","author":"SK Warfield","year":"2004","unstructured":"Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23(7), 903\u2013921 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"6_CR21","unstructured":"Yang, Y., Wang, Z., Liu, J., Cheng, K.T., Yang, X.: Label refinement with an iterative generative adversarial network for boosting retinal vessel segmentation. arXiv preprint arXiv:1912.02589 (2019)"},{"key":"6_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-3-030-59710-8_18","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"L Zhang","year":"2020","unstructured":"Zhang, L., et al.: Learning to Segment When Experts Disagree. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 179\u2013190. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_18"},{"key":"6_CR23","unstructured":"Zhang, L., et al.: Disentangling human error from the ground truth in segmentation of medical images. In: Proceedings of the 34th International Conference on Neural Information Processing Systems, pp. 15750\u201315762. ACL (2020)"},{"key":"6_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/978-3-030-59710-8_70","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"M Zhang","year":"2020","unstructured":"Zhang, M., et al.: Characterizing label errors: confident learning for noisy-labeled image segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 721\u2013730. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_70"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Zhen, L., Hu, P., Wang, X., Peng, D.: Deep supervised cross-modal retrieval. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10394\u201310403 (2019)","DOI":"10.1109\/CVPR.2019.01064"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, G., Awadallah, A.H., Dumais, S.: Meta label correction for noisy label learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 11053\u201311061 (2021)","DOI":"10.1609\/aaai.v35i12.17319"}],"container-title":["Lecture Notes in Computer Science","Medical Image Learning with Limited and Noisy Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44917-8_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T20:34:55Z","timestamp":1696710895000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44917-8_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031471964","9783031449178"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44917-8_6","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":"8 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MILLanD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Medical Image Learning with Limited and Noisy Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"milland2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccaimilland.wixsite.com\/milland2023","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":"38","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":"25","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":"66% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}