{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T07:33:29Z","timestamp":1775115209177,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031456725","type":"print"},{"value":"9783031456732","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-45673-2_7","type":"book-chapter","created":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T08:02:16Z","timestamp":1697270536000},"page":"62-71","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["unORANIC: Unsupervised Orthogonalization of\u00a0Anatomy and\u00a0Image-Characteristic Features"],"prefix":"10.1007","author":[{"given":"Sebastian","family":"Doerrich","sequence":"first","affiliation":[]},{"given":"Francesco","family":"Di Salvo","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Ledig","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,15]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Acevedo, A., Merino, A., Alf\u00e9rez, S., \u00c1ngel Molina, Bold\u00fa, L., Rodellar, J.: A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data Brief 30, 105474 (2020)","DOI":"10.1016\/j.dib.2020.105474"},{"key":"7_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)","journal-title":"Data Brief"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11, 125 (2020)","DOI":"10.3390\/info11020125"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Chartsias, A., et al.: Disentangled representation learning in cardiac image analysis. Med. Image Anal. 58, 101535 (2019)","DOI":"10.1016\/j.media.2019.101535"},{"key":"7_CR5","unstructured":"Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (isic). arXiv:1902.03368 (2019)"},{"key":"7_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1007\/978-3-030-59728-3_70","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"BE Dewey","year":"2020","unstructured":"Dewey, B.E., et al.: A disentangled latent space for cross-site MRI harmonization. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 720\u2013729. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_70"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Eche, T., Schwartz, L.H., Mokrane, F.Z., Dercle, L.: Toward generalizability in the deployment of artificial intelligence in radiology: Role of computation stress testing to overcome underspecification. Radiol. Artificial Intell. 3, e210097 (2021)","DOI":"10.1148\/ryai.2021210097"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2016-December, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"7_CR9","unstructured":"Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. In: Proceedings of the International Conference on Learning Representations (2019)"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Jeong, J., Zou, Y., Kim, T., Zhang, D., Ravichandran, A., Dabeer, O.: Winclip: zero-\/few-shot anomaly classification and segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19606\u201319616 (2023)","DOI":"10.1109\/CVPR52729.2023.01878"},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122-1131.e9 (2018)","journal-title":"Cell"},{"key":"7_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2022.100127","volume":"13","author":"A Khan","year":"2022","unstructured":"Khan, A., et al.: Impact of scanner variability on lymph node segmentation in computational pathology. J. Pathol. Inf. 13, 100127 (2022)","journal-title":"J. Pathol. Inf."},{"key":"7_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-3-319-67558-9_10","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"MW Lafarge","year":"2017","unstructured":"Lafarge, M.W., Pluim, J.P.W., Eppenhof, K.A.J., Moeskops, P., Veta, M.: Domain-Adversarial Neural Networks to\u00a0Address the Appearance Variability of\u00a0Histopathology Images. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 83\u201391. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_10"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Li, B., Wang, Y., Zhang, S., Li, D., Keutzer, K., Darrell, T., Zhao, H.: Learning invariant representations and risks for semi-supervised domain adaptation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1104\u20131113 (2021)","DOI":"10.1109\/CVPR46437.2021.00116"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Learning to generalize: Meta-learning for domain generalization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, pp. 3490\u20133497 (2018)","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"7_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100512","volume":"3","author":"R Liu","year":"2022","unstructured":"Liu, R., et al.: Deepdrid: diabetic retinopathy-grading and image quality estimation challenge. Patterns 3, 100512 (2022)","journal-title":"Patterns"},{"key":"7_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106791","volume":"157","author":"ON Manzari","year":"2023","unstructured":"Manzari, O.N., Ahmadabadi, H., Kashiani, H., Shokouhi, S.B., Ayatollahi, A.: MedVit: a robust vision transformer for generalized medical image classification. Comput. Biol. Med. 157, 106791 (2023)","journal-title":"Comput. Biol. Med."},{"key":"7_CR18","unstructured":"Michaelis, C., et al.: Benchmarking robustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484 (2019)"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Ngo, P.C., Winarto, A.A., Kou, C.K.L., Park, S., Akram, F., Lee, H.K.: Fence GAN: towards better anomaly detection. In: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI 2019-November, pp. 141\u2013148 (2019)","DOI":"10.1109\/ICTAI.2019.00028"},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"4001","DOI":"10.1109\/TMI.2020.3008930","volume":"39","author":"I Oksuz","year":"2020","unstructured":"Oksuz, I., et al.: Deep learning-based detection and correction of cardiac MR motion artefacts during reconstruction for high-quality segmentation. IEEE Trans. Med. Imaging 39, 4001\u20134010 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Priyanka, Kumar, D.: Feature extraction and selection of kidney ultrasound images using GLCM and PCA. Procedia Comput. Sci. 167, 1722\u20131731 (2020)","DOI":"10.1016\/j.procs.2020.03.382"},{"key":"7_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/978-3-030-01234-2_10","volume-title":"Computer Vision \u2013 ECCV 2018","author":"T Robert","year":"2018","unstructured":"Robert, T., Thome, N., Cord, M.: HybridNet: classification and reconstruction cooperation for semi-supervised learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 158\u2013175. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_10"},{"key":"7_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107021","volume":"161","author":"A Rondinella","year":"2023","unstructured":"Rondinella, A., et al.: Boosting multiple sclerosis lesion segmentation through attention mechanism. Comput. Biol. Med. 161, 107021 (2023)","journal-title":"Comput. Biol. Med."},{"key":"7_CR24","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"},{"key":"7_CR25","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1109\/JBHI.2020.3032060","volume":"25","author":"K Stacke","year":"2021","unstructured":"Stacke, K., Eilertsen, G., Unger, J., Lundstrom, C.: Measuring domain shift for deep learning in histopathology. IEEE J. Biomed. Health Inform. 25, 325\u2013336 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"7_CR26","doi-asserted-by":"crossref","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1\u20139 (2018)","DOI":"10.1038\/sdata.2018.161"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Yang, J., Shi, R., Ni, B.: Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis. In: Proceedings - International Symposium on Biomedical Imaging 2021-April, pp. 191\u2013195 (2020)","DOI":"10.1109\/ISBI48211.2021.9434062"},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Yang, J., et al.: Medmnist v2 - a large-scale lightweight benchmark for 2d and 3d biomedical image classification. Sci. Data 10(1), 1\u201310 (2023)","DOI":"10.1038\/s41597-022-01721-8"},{"key":"7_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118569","volume":"243","author":"L Zuo","year":"2021","unstructured":"Zuo, L., et al.: Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory. Neuroimage 243, 118569 (2021)","journal-title":"Neuroimage"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45673-2_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:52:56Z","timestamp":1710348776000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45673-2_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,15]]},"ISBN":["9783031456725","9783031456732"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45673-2_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,15]]},"assertion":[{"value":"15 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning 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":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2023?pli=1","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":"139","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":"93","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":"67% - 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":"2","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}