{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T20:26:26Z","timestamp":1775766386500,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031723896","type":"print"},{"value":"9783031723902","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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\/978-3-031-72390-2_9","type":"book-chapter","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T10:03:14Z","timestamp":1729591394000},"page":"87-97","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Feature Extraction for\u00a0Generative Medical Imaging Evaluation: New Evidence Against an\u00a0Evolving Trend"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0995-3695","authenticated-orcid":false,"given":"McKell","family":"Woodland","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3015-3570","authenticated-orcid":false,"given":"Austin","family":"Castelo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9298-6644","authenticated-orcid":false,"given":"Mais","family":"Al Taie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0616-982X","authenticated-orcid":false,"given":"Jessica","family":"Albuquerque Marques Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2078-4718","authenticated-orcid":false,"given":"Mohamed","family":"Eltaher","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9264-9725","authenticated-orcid":false,"given":"Frank","family":"Mohn","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7450-2702","authenticated-orcid":false,"given":"Alexander","family":"Shieh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-4875","authenticated-orcid":false,"given":"Suprateek","family":"Kundu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3752-5997","authenticated-orcid":false,"given":"Joshua P.","family":"Yung","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9678-496X","authenticated-orcid":false,"given":"Ankit B.","family":"Patel","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9364-5040","authenticated-orcid":false,"given":"Kristy K.","family":"Brock","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"9_CR1","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Guyon, I., et\u00a0al. (eds.) NIPS. vol.\u00a030. Curran Associates, Inc. (2017)"},{"key":"9_CR2","doi-asserted-by":"publisher","unstructured":"Woodland, M., et\u00a0al.: Evaluating the performance of stylegan2-ada on medical images. In: Zhao, C., et\u00a0al. (eds.) SASHIMI. pp. 142\u2013153. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16980-9_14","DOI":"10.1007\/978-3-031-16980-9_14"},{"key":"9_CR3","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.cviu.2018.10.009","volume":"179","author":"A Borji","year":"2019","unstructured":"Borji, A.: Pros and cons of gan evaluation measures. Comput. Vis. Image Underst. 179, 41\u201365 (2019). https:\/\/doi.org\/10.1016\/j.cviu.2018.10.009","journal-title":"Comput. Vis. Image Underst."},{"key":"9_CR4","unstructured":"Truong, T., Mohammadi, S., Lenga, M.: How transferable are self-supervised features in medical image classification tasks? In: Jung, K., et\u00a0al. (eds.) MLHC. vol.\u00a0158, pp. 54\u201374. PMLR (2021)"},{"key":"9_CR5","unstructured":"Kynk\u00e4\u00e4nniemi, T., Karras, T., Aittala, M., Aila, T., Lehtinen, J.: The role of imagenet classes in fr\u00e9chet inception distance. arXiv:2203.06026 (2023)"},{"key":"9_CR6","doi-asserted-by":"publisher","unstructured":"Mei, X., et\u00a0al.: Radimagenet: An open radiologic deep learning research dataset for effective transfer learning. Radiol.: Artif. Intell. 4(5) (2022). https:\/\/doi.org\/10.1148\/ryai.210315","DOI":"10.1148\/ryai.210315"},{"issue":"6","key":"9_CR7","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.10.6.061403","volume":"10","author":"R Osuala","year":"2023","unstructured":"Osuala, R., et\u00a0al.: medigan: a Python library of pretrained generative models for medical image synthesis. J. Med. Imaging 10(6), 061403 (2023). https:\/\/doi.org\/10.1117\/1.JMI.10.6.061403","journal-title":"J. Med. Imaging"},{"issue":"12","key":"9_CR8","doi-asserted-by":"publisher","first-page":"320","DOI":"10.3390\/jimaging8120320","volume":"8","author":"J Anton","year":"2022","unstructured":"Anton, J., et\u00a0al.: How well do self-supervised models transfer to medical imaging? J. Imaging 8(12), \u00a0320 (2022). https:\/\/doi.org\/10.3390\/jimaging8120320","journal-title":"J. Imaging"},{"key":"9_CR9","unstructured":"Morozov, S., Voynov, A., Babenko, A.: On self-supervised image representations for gan evaluation. In: ICLR (2020)"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Zabih, R., et\u00a0al. (eds.) CVPR. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Chen, J., Wei, J., Li, R.: Targan: target-aware generative adversarial networks for multi-modality medical image translation. In: de\u00a0Bruijne, M., et\u00a0al. (eds.) MICCAI. pp. 24\u201333. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-87231-1_3","DOI":"10.1007\/978-3-030-87231-1_3"},{"key":"9_CR12","doi-asserted-by":"publisher","unstructured":"Jung, E., Luna, M., Park, S.H.: Conditional gan with an attention-based generator and a 3d discriminator for 3d medical image generation. In: de\u00a0Bruijne, M., et\u00a0al. (eds.) MICCAI. pp. 318\u2013328. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-87231-1_31","DOI":"10.1007\/978-3-030-87231-1_31"},{"key":"9_CR13","doi-asserted-by":"publisher","unstructured":"Tronchin, L., Sicilia, R., Cordelli, E., Ramella, S., Soda, P.: Evaluating gans in medical imaging. In: Engelhardt, S., et\u00a0al. (eds.) DGM4MICCAI, DALI. pp. 112\u2013121. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-88210-5_10","DOI":"10.1007\/978-3-030-88210-5_10"},{"issue":"8","key":"9_CR14","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1109\/TMI.2009.2013851","volume":"28","author":"T Heimann","year":"2009","unstructured":"Heimann, T., et\u00a0al.: Comparison and evaluation of methods for liver segmentation from ct datasets. IEEE Trans. Med. Imaging 28(8), 1251\u20131265 (2009). https:\/\/doi.org\/10.1109\/TMI.2009.2013851","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Wang, X., et\u00a0al.: Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Chellappa, R., et\u00a0al. (eds.) CVPR. IEEE (2017)","DOI":"10.1109\/CVPR.2017.369"},{"issue":"1","key":"9_CR16","doi-asserted-by":"publisher","first-page":"4128","DOI":"10.1038\/s41467-022-30695-9","volume":"13","author":"M Antonelli","year":"2022","unstructured":"Antonelli, M., et\u00a0al.: The medical segmentation decathlon. Nat. Commun. 13(1), \u00a04128 (2022). https:\/\/doi.org\/10.1038\/s41467-022-30695-9","journal-title":"The medical segmentation decathlon. Nat. Commun."},{"key":"9_CR17","unstructured":"Simpson, A.L., et\u00a0al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv:1902.09063 (2019)"},{"issue":"11","key":"9_CR18","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., et\u00a0al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018). https:\/\/doi.org\/10.1109\/TMI.2018.2837502","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Karras, T., et\u00a0al.: Analyzing and improving the image quality of stylegan. In: Zabih, R., et\u00a0al. (eds.) CVPR. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"9_CR20","unstructured":"Karras, T., et\u00a0al.: Training generative adversarial networks with limited data. In: Larochelle, H., et\u00a0al. (eds.) NeurIPS. vol.\u00a033, pp. 12104\u201312114. Curran Associates, Inc. (2020)"},{"key":"9_CR21","unstructured":"Zhao, S., Liu, Z., Lin, J., Zhu, J.Y., Han, S.: Differentiable augmentation for data-efficient gan training. In: Larochelle, H., et\u00a0al. (eds.) NeurIPS. vol.\u00a033, pp. 7559\u20137570. Curran Associates, Inc. (2020)"},{"key":"9_CR22","unstructured":"Jiang, L., Dai, B., Wu, W., Loy, C.C.: Deceive d: Adaptive pseudo augmentation for gan training with limited data. In: Ranzato, M. (ed.) NeurIPS. vol.\u00a034, pp. 21655\u201321667. Curran Associates, Inc. (2021)"},{"issue":"3","key":"9_CR23","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/0047-259X(82)90077-X","volume":"12","author":"D Dowson","year":"1982","unstructured":"Dowson, D., Landau, B.: The fr\u00e9chet distance between multivariate normal distributions. J. Multivar. Anal. 12(3), 450\u2013455 (1982). https:\/\/doi.org\/10.1016\/0047-259X(82)90077-X","journal-title":"J. Multivar. Anal."},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et\u00a0al.: Going deeper with convolutions. In: Bischof, H., et\u00a0al. (eds.) CVPR. IEEE (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Zabih, R., et\u00a0al. (eds.) CVPR. IEEE (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"9_CR26","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. vol.\u00a031 (2017). https:\/\/doi.org\/10.1609\/aaai.v31i1.11231","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van\u00a0der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR. IEEE (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"9_CR28","doi-asserted-by":"publisher","unstructured":"Deng, J., et\u00a0al.: Imagenet: A large-scale hierarchical image database. In: CVPR. pp. 248\u2013255. IEEE (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"9_CR29","unstructured":"Caron, M., et\u00a0al.: Unsupervised learning of visual features by contrasting cluster assignments. In: NeurIPS. vol.\u00a033, pp. 9912\u20139924. Curran Associates, Inc. (2020)"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Caron, M., et\u00a0al.: Emerging properties in self-supervised vision transformers. In: Berg, T., et\u00a0al. (eds.) ICCV. pp. 9650\u20139660. IEEE (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"9_CR31","unstructured":"Li, Z., Wang, Y., Yu, J.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Berg, T., et\u00a0al. (eds.) ICCV. pp. 10012\u201310022. IEEE (2021)"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, H.Y., Lu, C., Yang, S., Yu, Y.: Convnets vs. transformers: Whose visual representations are more transferable? In: Vandenhende, S., et\u00a0al. (eds.) ICCV Workshops. pp. 2230\u20132238. IEEE (2021)","DOI":"10.1109\/ICCVW54120.2021.00252"},{"issue":"12","key":"9_CR33","doi-asserted-by":"publisher","first-page":"15725","DOI":"10.1109\/TPAMI.2023.3306436","volume":"45","author":"M Kang","year":"2023","unstructured":"Kang, M., Shim, W., Cho, M., Park, J.: Studiogan: A taxonomy and benchmark of gans for image synthesis. Trans. Pattern Anal. Mach. Intell. 45(12), 15725\u201315742 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2023.3306436","journal-title":"Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72390-2_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T10:04:19Z","timestamp":1729591459000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72390-2_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723896","9783031723902"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72390-2_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}