{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:56:21Z","timestamp":1743000981252,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031452482"},{"type":"electronic","value":"9783031452499"}],"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\/978-3-031-45249-9_25","type":"book-chapter","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T09:40:34Z","timestamp":1696844434000},"page":"256-265","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Study of\u00a0Age and\u00a0Sex Bias in\u00a0Multiple Instance Learning Based Classification of\u00a0Acute Myeloid Leukemia Subtypes"],"prefix":"10.1007","author":[{"given":"Ario","family":"Sadafi","sequence":"first","affiliation":[]},{"given":"Matthias","family":"Hehr","sequence":"additional","affiliation":[]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[]},{"given":"Carsten","family":"Marr","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,9]]},"reference":[{"issue":"4","key":"25_CR1","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1056\/NEJM199107253250401","volume":"325","author":"JZ Ayanian","year":"1991","unstructured":"Ayanian, J.Z., Epstein, A.M.: Differences in the use of procedures between women and men hospitalized for coronary heart disease. N. Engl. J. Med. 325(4), 221\u2013225 (1991)","journal-title":"N. Engl. J. Med."},{"key":"25_CR2","first-page":"3","volume":"8","author":"C Bonferroni","year":"1936","unstructured":"Bonferroni, C.: Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze 8, 3\u201362 (1936)","journal-title":"Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze"},{"issue":"3","key":"25_CR3","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1080\/00401706.1964.10490181","volume":"6","author":"OJ Dunn","year":"1964","unstructured":"Dunn, O.J.: Multiple comparisons using rank sums. Technometrics 6(3), 241\u2013252 (1964)","journal-title":"Technometrics"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"25_CR5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000187","volume":"2","author":"M Hehr","year":"2023","unstructured":"Hehr, M., et al.: Explainable AI identifies diagnostic cells of genetic AML subtypes. PLOS Digit. Health 2(3), e0000187 (2023)","journal-title":"PLOS Digit. Health"},{"key":"25_CR6","doi-asserted-by":"publisher","DOI":"10.7937\/6PPE-4020","author":"M Hehr","year":"2023","unstructured":"Hehr, M., et al.: A morphological dataset of white blood cells from patients with four different genetic AML entities and non-malignant controls (AML-Cytomorphology_MLL_Helmholtz) (version 1) [data set]. Cancer Imaging Arch. (2023). https:\/\/doi.org\/10.7937\/6PPE-4020","journal-title":"Cancer Imaging Arch."},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Hiremath, P., Bannigidad, P., Geeta, S.: Automated identification and classification of white blood cells (leukocytes) in digital microscopic images. IJCA special issue on \u201crecent trends in image processing and pattern recognition\u201d RTIPPR, pp. 59\u201363 (2010)","DOI":"10.1117\/12.853303"},{"key":"25_CR8","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127\u20132136. PMLR (2018)"},{"key":"25_CR9","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-031-17899-3_2","volume-title":"MLCN 2022","author":"S Ioannou","year":"2022","unstructured":"Ioannou, S., Chockler, H., Hammers, A., King, A.P., Initiative, A.D.N.: A study of demographic bias in CNN-based brain MR segmentation. In: Abdulkadir, A., et al. (eds.) MLCN 2022. LNCS, vol. 13596, pp. 13\u201322. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-17899-3_2"},{"issue":"260","key":"25_CR10","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","volume":"47","author":"WH Kruskal","year":"1952","unstructured":"Kruskal, W.H., Wallis, W.A.: Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47(260), 583\u2013621 (1952)","journal-title":"J. Am. Stat. Assoc."},{"key":"25_CR11","unstructured":"Lara, M.A.R., Mosquera, C., Ferrante, E., Echeveste, R.: Towards unraveling calibration biases in medical image analysis. arXiv preprint arXiv:2305.05101 (2023)"},{"key":"25_CR12","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/978-3-031-23443-9_22","volume-title":"STACOM 2022","author":"T Lee","year":"2022","unstructured":"Lee, T., Puyol-Ant\u00f3n, E., Ruijsink, B., Shi, M., King, A.P.: A systematic study of race and sex bias in CNN-based cardiac MR segmentation. In: Camara, O., et al. (eds.) STACOM 2022. LNCS, vol. 13593, pp. 233\u2013244. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-23443-9_22"},{"issue":"17","key":"25_CR13","first-page":"3171","volume":"116","author":"F Lo-Coco","year":"2010","unstructured":"Lo-Coco, F., et al.: Front-line treatment of acute promyelocytic leukemia with AIDA induction followed by risk-adapted consolidation for adults younger than 61 years: results of the AIDA-2000 trial of the gimema group. Blood J. Am. Soc. Hematol. 116(17), 3171\u20133179 (2010)","journal-title":"Blood J. Am. Soc. Hematol."},{"issue":"11","key":"25_CR14","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1038\/s42256-019-0101-9","volume":"1","author":"C Matek","year":"2019","unstructured":"Matek, C., Schwarz, S., Spiekermann, K., Marr, C.: Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks. Nat. Mach. Intell. 1(11), 538\u2013544 (2019)","journal-title":"Nat. Mach. Intell."},{"issue":"20","key":"25_CR15","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1001\/jama.2017.16230","volume":"318","author":"RW Parsa-Parsi","year":"2017","unstructured":"Parsa-Parsi, R.W.: The revised declaration of Geneva: a modern-day physician\u2019s pledge. JAMA 318(20), 1971\u20131972 (2017)","journal-title":"JAMA"},{"key":"25_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.859310","volume":"9","author":"E Puyol-Ant\u00f3n","year":"2022","unstructured":"Puyol-Ant\u00f3n, E., et al.: Fairness in cardiac magnetic resonance imaging: assessing sex and racial bias in deep learning-based segmentation. Front. Cardiovasc. Med. 9, 859310 (2022)","journal-title":"Front. Cardiovasc. Med."},{"key":"25_CR17","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"25_CR18","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/978-3-031-34048-2_14","volume-title":"IPMI 2023","author":"A Sadafi","year":"2023","unstructured":"Sadafi, A., et al.: Pixel-level explanation of multiple instance learning models in biomedical single cell images. In: Frangi, A., de Bruijne, M., Wassermann, D., Navab, N. (eds.) IPMI 2023. LNCS, vol. 13939, pp. 170\u2013182. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-34048-2_14"},{"key":"25_CR19","doi-asserted-by":"publisher","first-page":"1058720","DOI":"10.3389\/fphys.2023.1058720","volume":"14","author":"A Sadafi","year":"2023","unstructured":"Sadafi, A., Bordukova, M., Makhro, A., Navab, N., Bogdanova, A., Marr, C.: RedTell: an AI tool for interpretable analysis of red blood cell morphology. Front. Physiol. 14, 1058720 (2023)","journal-title":"Front. Physiol."},{"key":"25_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/978-3-030-32239-7_76","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"A Sadafi","year":"2019","unstructured":"Sadafi, A., et al.: Multiclass deep active learning for detecting red blood cell subtypes in brightfield microscopy. In: Shen, D., et al. (eds.) MICCAI 2019, Part I. LNCS, vol. 11764, pp. 685\u2013693. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_76"},{"key":"25_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1007\/978-3-030-59722-1_24","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"A Sadafi","year":"2020","unstructured":"Sadafi, A., et al.: Attention based multiple instance learning for classification of blood cell disorders. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 246\u2013256. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_24"},{"key":"25_CR22","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/978-3-031-16437-8_71","volume-title":"MICCAI 2022","author":"R Salehi","year":"2022","unstructured":"Salehi, R., et al.: Unsupervised cross-domain feature extraction for single blood cell image classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13433, pp. 739\u2013748. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16437-8_71"},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Sharma, S., et al.: Deep learning model for the automatic classification of white blood cells. Comput. Intell. Neurosci. 2022 (2022)","DOI":"10.1155\/2022\/7384131"},{"issue":"1","key":"25_CR24","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1038\/s41698-021-00179-y","volume":"5","author":"JW Sidhom","year":"2021","unstructured":"Sidhom, J.W., et al.: Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features. NPJ Precis. Oncol. 5(1), 38 (2021)","journal-title":"NPJ Precis. Oncol."}],"container-title":["Lecture Notes in Computer Science","Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45249-9_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:00:08Z","timestamp":1710255608000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45249-9_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031452482","9783031452499"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45249-9_25","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":"9 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FAIMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Fairness of AI in Medical Imaging","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":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"faimi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/faimi-workshop.github.io\/2023-miccai\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}