{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T13:18:51Z","timestamp":1754486331048,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031178986"},{"type":"electronic","value":"9783031178993"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-17899-3_7","type":"book-chapter","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T12:05:03Z","timestamp":1665144303000},"page":"63-72","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Weakly Supervised Intracranial Hemorrhage Segmentation Using Hierarchical Combination of\u00a0Attention Maps from\u00a0a\u00a0Swin Transformer"],"prefix":"10.1007","author":[{"given":"Amirhossein","family":"Rasoulian","sequence":"first","affiliation":[]},{"given":"Soorena","family":"Salari","sequence":"additional","affiliation":[]},{"given":"Yiming","family":"Xiao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,6]]},"reference":[{"issue":"3","key":"7_CR1","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1161\/STROKEAHA.120.030930","volume":"52","author":"T Apostolaki-Hansson","year":"2021","unstructured":"Apostolaki-Hansson, T., Ullberg, T., Pihlsg\u00e5rd, M., Norrving, B., Petersson, J.: Prognosis of intracerebral hemorrhage related to antithrombotic use: an observational study from the Swedish stroke register (riksstroke). Stroke 52(3), 966\u2013974 (2021)","journal-title":"Stroke"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Chefer, H., Gur, S., Wolf, L.: Transformer interpretability beyond attention visualization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 782\u2013791 (2021)","DOI":"10.1109\/CVPR46437.2021.00084"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Cho, J., et al.: Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models. J. Digit. Imaging 32 450\u2013461 (2018)","DOI":"10.1007\/s10278-018-00172-1"},{"issue":"8","key":"7_CR4","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.3390\/diagnostics11081384","volume":"11","author":"Y Dai","year":"2021","unstructured":"Dai, Y., Gao, Y., Liu, F.: Transmed: transformers advance multi-modal medical image classification. Diagnostics 11(8), 1384 (2021)","journal-title":"Diagnostics"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Dalmaz, O., Yurt, M., \u00c7ukur, T.: Resvit: Residual vision transformers for multi-modal medical image synthesis. IEEE Trans. Med. Imaging, 1 (2022)","DOI":"10.1109\/TMI.2022.3167808"},{"key":"7_CR6","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2021)"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Flanders, A.E., et al.: Construction of a machine learning dataset through collaboration: the RSNA 2019 brain CT hemorrhage challenge. Radiol.: Artif. Intell. 2(3) (2020)","DOI":"10.1148\/ryai.2020209002"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Gu, Y., Yang, K., Fu, S., Chen, S., Li, X., Marsic, I.: Multimodal affective analysis using hierarchical attention strategy with word-level alignment. In: Proceedings of the Conference Association for Computational Linguistics Meeting vol. 2018, p. 2225. NIH Public Access (2018)","DOI":"10.18653\/v1\/P18-1207"},{"issue":"1","key":"7_CR9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/data5010014","volume":"5","author":"MD Hssayeni","year":"2020","unstructured":"Hssayeni, M.D., Croock, M.S., Salman, A.D., Al-khafaji, H.F., Yahya, Z.A., Ghoraani, B.: Intracranial hemorrhage segmentation using a deep convolutional model. Data 5(1), 14 (2020)","journal-title":"Data"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Nemcek, J., Vicar, T., Jakubicek, R.: Weakly supervised deep learning-based intracranial hemorrhage localization. arXiv preprint arXiv:2105.00781 (2021)","DOI":"10.5220\/0010825000003123"},{"issue":"3","key":"7_CR13","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1007\/s12028-011-9538-3","volume":"15","author":"A Qureshi","year":"2011","unstructured":"Qureshi, A., Palesch, Y.: Antihypertensive treatment of acute cerebral hemorrhage (ATACH) ii: design, methods, and rationale. Neurocrit. Care 15(3), 559\u2013576 (2011)","journal-title":"Neurocrit. Care"},{"key":"7_CR14","unstructured":"Rajashekar, D., Liang, J.W.: Intracerebral hemorrhage. In: StatPearls [Internet]. StatPearls Publishing (2021)"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Salehinejad, H., et al.: A real-world demonstration of machine learning generalizability in the detection of intracranial hemorrhage on head computerized tomography. Scientific Reports 11(17051) (2021)","DOI":"10.1038\/s41598-021-95533-2"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"7_CR17","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1109\/TIP.2019.2928634","volume":"29","author":"VA Sindagi","year":"2019","unstructured":"Sindagi, V.A., Patel, V.M.: Ha-CNN: Hierarchical attention-based crowd counting network. IEEE Trans. Image Process. 29, 323\u2013335 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"7_CR18","unstructured":"Wightman, R.: Pytorch image models. https:\/\/github.com\/rwightman\/pytorch-image-models (2019)"},{"key":"7_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/978-3-030-32248-9_24","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"K Wu","year":"2019","unstructured":"Wu, K., Du, B., Luo, M., Wen, H., Shen, Y., Feng, J.: Weakly supervised brain lesion segmentation via attentional representation learning. In: Shen, D. (ed.) MICCAI 2019. LNCS, vol. 11766, pp. 211\u2013219. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_24"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480\u20131489 (2016)","DOI":"10.18653\/v1\/N16-1174"},{"issue":"11","key":"7_CR21","doi-asserted-by":"publisher","first-page":"6191","DOI":"10.1007\/s00330-019-06163-2","volume":"29","author":"H Ye","year":"2019","unstructured":"Ye, H., et al.: Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. Eur. Radiol. 29(11), 6191\u20136201 (2019). https:\/\/doi.org\/10.1007\/s00330-019-06163-2","journal-title":"Eur. Radiol."},{"issue":"1","key":"7_CR22","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1093\/nsr\/nwx106","volume":"5","author":"ZH Zhou","year":"2017","unstructured":"Zhou, Z.H.: A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5(1), 44\u201353 (2017)","journal-title":"Natl. Sci. Rev."}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Clinical Neuroimaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17899-3_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:26:26Z","timestamp":1710260786000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17899-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031178986","9783031178993"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17899-3_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"6 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLCN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Clinical Neuroimaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlcn2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlcnws.com\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","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":"17","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":"74% - 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":"2.6","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)"}}]}}