{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T06:17:25Z","timestamp":1758349045047,"version":"3.44.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049360"},{"type":"electronic","value":"9783032049377"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04937-7_54","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:41:38Z","timestamp":1758260498000},"page":"568-578","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["StyleGAN-Based Brain MRI Anomaly Detection via\u00a0Latent Code Retrieval and\u00a0Partial Swap"],"prefix":"10.1007","author":[{"given":"Jie","family":"Wei","sequence":"first","affiliation":[]},{"given":"Xiaofei","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Shaoting","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Guotai","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"54_CR1","doi-asserted-by":"crossref","unstructured":"Abdal, R., Qin, Y., Wonka, P.: Image2StyleGAN: how to embed images into the StyleGAN latent space? In: ICCV, pp. 4432\u20134441 (2019)","DOI":"10.1109\/ICCV.2019.00453"},{"key":"54_CR2","doi-asserted-by":"crossref","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: Ganomaly: semi-supervised anomaly detection via adversarial training. In: ACCV, pp. 622\u2013637 (2019)","DOI":"10.1007\/978-3-030-20893-6_39"},{"issue":"1","key":"54_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 1\u201313 (2017)","journal-title":"Sci. Data"},{"key":"54_CR4","unstructured":"Bakas, S., et\u00a0al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629 (2018)"},{"key":"54_CR5","doi-asserted-by":"crossref","unstructured":"Behrendt, F., et al.: Leveraging the mahalanobis distance to enhance unsupervised brain MRI anomaly detection. In: MICCAI, pp. 394\u2013404 (2024)","DOI":"10.1007\/978-3-031-72120-5_37"},{"key":"54_CR6","doi-asserted-by":"crossref","unstructured":"Bercea, C.I., Wiestler, B., Rueckert, D., Schnabel, J.A.: Reversing the abnormal: pseudo-healthy generative networks for anomaly detection. In: MICCAI, pp. 293\u2013303 (2023)","DOI":"10.1007\/978-3-031-43904-9_29"},{"key":"54_CR7","doi-asserted-by":"crossref","unstructured":"Bercea, C.I., Wiestler, B., Rueckert, D., Schnabel, J.A.: Diffusion models with implicit guidance for medical anomaly detection. In: MICCAI, pp. 211\u2013220 (2024)","DOI":"10.1007\/978-3-031-72120-5_20"},{"key":"54_CR8","doi-asserted-by":"crossref","unstructured":"Defard, T., Setkov, A., Loesch, A., Audigier, R.: PaDiM: a patch distribution modeling framework for anomaly detection and localization. In: ICPR, pp. 475\u2013489 (2021)","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"54_CR9","doi-asserted-by":"publisher","first-page":"119637","DOI":"10.1016\/j.neuroimage.2022.119637","volume":"263","author":"B Dufumier","year":"2022","unstructured":"Dufumier, B., Grigis, A., Victor, J., Ambroise, C., Frouin, V., Duchesnay, E.: OpenBHB: a large-scale multi-site brain MRI data-set for age prediction and debiasing. Neuroimage 263, 119637 (2022)","journal-title":"Neuroimage"},{"key":"54_CR10","doi-asserted-by":"publisher","first-page":"105536","DOI":"10.1016\/j.bspc.2023.105536","volume":"88","author":"X Feng","year":"2024","unstructured":"Feng, X., Lin, J., Feng, C.M., Lu, G.: GAN inversion-based semi-supervised learning for medical image segmentation. Biomed. Signal Process. Control 88, 105536 (2024)","journal-title":"Biomed. Signal Process. Control"},{"issue":"2","key":"54_CR11","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: Freesurfer. Neuroimage 62(2), 774\u2013781 (2012)","journal-title":"Neuroimage"},{"key":"54_CR12","doi-asserted-by":"crossref","unstructured":"Gong, D., et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: ICCV, pp. 1705\u20131714 (2019)","DOI":"10.1109\/ICCV.2019.00179"},{"issue":"11","key":"54_CR13","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"54_CR14","doi-asserted-by":"crossref","unstructured":"Hagag, A., et al.: Deep learning for cancer prognosis prediction using portrait photos by StyleGAN embedding. In: MICCAI, pp. 198\u2013208 (2024)","DOI":"10.1007\/978-3-031-72086-4_19"},{"issue":"12","key":"54_CR15","doi-asserted-by":"publisher","first-page":"3509","DOI":"10.1109\/TMI.2022.3187170","volume":"41","author":"DC Hochberg","year":"2022","unstructured":"Hochberg, D.C., Greenspan, H., Giryes, R.: A self supervised StyleGAN for image annotation and classification with extremely limited labels. IEEE Trans. Med. Imaging 41(12), 3509\u20133519 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"54_CR16","doi-asserted-by":"crossref","unstructured":"Huang, C., Jiang, A., Feng, J., Zhang, Y., Wang, X., Wang, Y.: Adapting visual-language models for generalizable anomaly detection in medical images. In: CVPR, pp. 11375\u201311385 (2024)","DOI":"10.1109\/CVPR52733.2024.01081"},{"key":"54_CR17","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: CVPR, pp. 8110\u20138119 (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"issue":"1","key":"54_CR18","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1038\/s41597-022-01401-7","volume":"9","author":"SL Liew","year":"2022","unstructured":"Liew, S.L., et al.: A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms. Sci. Data 9(1), 320 (2022)","journal-title":"Sci. Data"},{"key":"54_CR19","doi-asserted-by":"crossref","unstructured":"Liu, H., Song, Y., Chen, Q.: Delving StyleGAN inversion for image editing: a foundation latent space viewpoint. In: CVPR, pp. 10072\u201310082 (2023)","DOI":"10.1109\/CVPR52729.2023.00971"},{"issue":"10","key":"54_CR20","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"},{"issue":"2","key":"54_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. (CSUR) 54(2), 1\u201338 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"54_CR22","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: ICML, pp. 8748\u20138763 (2021)"},{"key":"54_CR23","doi-asserted-by":"publisher","first-page":"0005021","DOI":"10.2352\/J.Percept.Imaging.2022.5.000502","volume":"5","author":"Z Ren","year":"2022","unstructured":"Ren, Z., Stella, X.Y., Whitney, D.: Controllable medical image generation via GAN. J. Perceptual Imaging 5, 0005021 (2022)","journal-title":"J. Perceptual Imaging"},{"key":"54_CR24","doi-asserted-by":"crossref","unstructured":"Roth, K., Pemula, L., Zepeda, J., Sch\u00f6lkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: CVPR, pp. 14318\u201314328 (2022)","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"54_CR25","doi-asserted-by":"crossref","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: IPMI, pp. 146\u2013157 (2017)","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"54_CR26","doi-asserted-by":"crossref","unstructured":"Wei, J., Wang, G., Zhang, S.: Fine-grained medical image out-of-distribution detection through multi-view feature uncertainty and adversarial sample generation. Pattern Recogn. 111401 (2025)","DOI":"10.1016\/j.patcog.2025.111401"},{"issue":"3","key":"54_CR27","doi-asserted-by":"publisher","first-page":"3121","DOI":"10.1109\/TPAMI.2022.3181070","volume":"45","author":"W Xia","year":"2022","unstructured":"Xia, W., Zhang, Y., Yang, Y., Xue, J.H., Zhou, B., Yang, M.H.: Gan inversion: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(3), 3121\u20133138 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"54_CR28","doi-asserted-by":"crossref","unstructured":"Xiang, T., et al.: SQUID: deep feature in-painting for unsupervised anomaly detection. In: CVPR, pp. 23890\u201323901 (2023)","DOI":"10.1109\/CVPR52729.2023.02288"},{"key":"54_CR29","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: DR\u00c6M-a discriminatively trained reconstruction embedding for surface anomaly detection. In: ICCV, pp. 8330\u20138339 (2021)","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"54_CR30","doi-asserted-by":"publisher","first-page":"107706","DOI":"10.1016\/j.patcog.2020.107706","volume":"112","author":"V Zavrtanik","year":"2021","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: Reconstruction by inpainting for visual anomaly detection. Pattern Recogn. 112, 107706 (2021)","journal-title":"Pattern Recogn."},{"key":"54_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"54_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xu, M., Qiu, D., Yan, R., Lang, N., Zhou, X.: MediCLIP: adapting CLIP for few-shot medical image anomaly detection. In: MICCAI, pp. 458\u2013468 (2024)","DOI":"10.1007\/978-3-031-72120-5_43"},{"key":"54_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xu, M., Zhou, X.: RealNet: a feature selection network with realistic synthetic anomaly for anomaly detection. In: CVPR, pp. 16699\u201316708 (2024)","DOI":"10.1109\/CVPR52733.2024.01580"},{"key":"54_CR34","doi-asserted-by":"crossref","unstructured":"Zhu, J., et al.: Linkgan: linking GAN latents to pixels for controllable image synthesis. In: CVPR, pp. 7656\u20137666 (2023)","DOI":"10.1109\/ICCV51070.2023.00704"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04937-7_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:41:47Z","timestamp":1758260507000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04937-7_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049360","9783032049377"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04937-7_54","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}