{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:10:40Z","timestamp":1758759040696,"version":"3.44.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032054715","type":"print"},{"value":"9783032054722","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"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\/978-3-032-05472-2_16","type":"book-chapter","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T09:25:12Z","timestamp":1758705912000},"page":"161-170","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Resolution Invariant Autoencoder"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4212-2578","authenticated-orcid":false,"given":"Ashay","family":"Patel","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3005-4523","authenticated-orcid":false,"given":"Michela","family":"Antonelli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5694-5340","authenticated-orcid":false,"given":"Sebastien","family":"Ourselin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1284-2558","authenticated-orcid":false,"given":"M. Jorge","family":"Cardoso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Valiant, L.G.: A theory of the learnable. In: Communications of the ACM (1984)","DOI":"10.1145\/800057.808710"},{"key":"16_CR2","unstructured":"Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of Representations for Domain Adaptation, vol. 19. MIT Press (2006). https:\/\/proceedings.neurips.cc\/paper\/2006\/file\/b1b0432ceafb0ce714426e9114852ac7-Paper.pdf"},{"key":"16_CR3","doi-asserted-by":"publisher","unstructured":"Varoquaux, G., Cheplygina, V.: Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digit. Med. 5, 48 (2022). ISSN 2398-6352. https:\/\/doi.org\/10.1038\/s41746-022-00592-y","DOI":"10.1038\/s41746-022-00592-y"},{"issue":"10","key":"16_CR4","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"},{"key":"16_CR5","unstructured":"Antonelli, M., et al.: The medical segmentation decathlon, arXiv preprint arXiv:2106.05735 (2021)"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Kuijf, H.J., et al.: Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge. IEEE Trans. Med. Imaging 38(11), 2556\u20132568 (2019)","DOI":"10.1109\/TMI.2019.2905770"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Decuyper, M., Maebe, J., Holen, R.V., Vandenberghe, S.: Artificial intelligence with deep learning in nuclear medicine and radiology. EJNMMI Phys. 8, 81 (2021). https:\/\/doi.org\/10.1186\/s40658-021-00426-y","DOI":"10.1186\/s40658-021-00426-y"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Dinsdale, N.K., Bluemke, E., Sundaresan, V., Jenkinson, M., Smith, S.M., Namburete, A.I.: Challenges for machine learning in clinical translation of big data imaging studies. Neuron 110, 3866\u20133881 (2022). https:\/\/doi.org\/10.1016\/j.neuron.2022.09.012","DOI":"10.1016\/j.neuron.2022.09.012"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Billot, B., et al.: SynthSeg: segmentation of brain MRI scans of any contrast and resolution without retraining. Med. Image Anal. 86, 102789 (2023)","DOI":"10.1016\/j.media.2023.102789"},{"key":"16_CR10","doi-asserted-by":"publisher","unstructured":"Patel, A., et al.: . Geometry-invariant abnormality detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 300\u2013309. Springer Nature Switzerland, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43907-0_29","DOI":"10.1007\/978-3-031-43907-0_29"},{"key":"16_CR11","unstructured":"Patel, A., Graham, M.S., Goh, V., Ourselin, S., Cardoso, M.J.: Resolution and field of view invariant generative modelling with latent diffusion models. In: Medical Imaging with Deep Learning (2024)"},{"key":"16_CR12","doi-asserted-by":"publisher","unstructured":"Joutard, S., Pietsch, M, Prevost, R.: HyperSpace: hypernetworks for spacing-adaptive image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 339\u2013349. Springer Nature Switzerland, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72114-4_33","DOI":"10.1007\/978-3-031-72114-4_33"},{"key":"16_CR13","unstructured":"Kingma, D.P.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 2013"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"16_CR15","doi-asserted-by":"publisher","unstructured":"Pinaya, W.H., et al.: Brain imaging generation with latent diffusion models. In: MICCAI Workshop on Deep Generative Models, pp. 117\u2013126. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-18576-2_12","DOI":"10.1007\/978-3-031-18576-2_12"},{"issue":"7","key":"16_CR16","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1038\/s42256-024-00864-0","volume":"6","author":"PD Tudosiu","year":"2024","unstructured":"Tudosiu, P.D., et al.: Realistic morphology-preserving generative modelling of the brain. Nat. Mach. Intell. 6(7), 811\u2013819 (2024)","journal-title":"Nat. Mach. Intell."},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Vu Quoc, H., Tran Le Phuong, T., Trinh Xuan, M., Dinh Viet, S.: LSegDiff: a latent diffusion model for medical image segmentation. In: Proceedings of the 12th International Symposium on Information and Communication Technology, pp. 456\u2013462 (2023)","DOI":"10.1145\/3628797.3629010"},{"key":"16_CR18","unstructured":"Luo, F., Xiang, J., Zhang, J., Han, X., Yang, W.: Image super-resolution via latent diffusion: a sampling-space mixture of experts and frequency-augmented decoder approach. arXiv preprint arXiv:2310.12004 (2023)"},{"key":"16_CR19","doi-asserted-by":"publisher","unstructured":"Patel, A., et al.: Cross attention transformers for multi-modal unsupervised whole-body pet anomaly detection. In: MICCAI Workshop on Deep Generative Models, pp. 14\u201323. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-18576-2_2","DOI":"10.1007\/978-3-031-18576-2_2"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Graham, M.S., et al.: Latent transformer models for out-of-distribution detection. Med. Image Anal. 90, 102967 (2023)","DOI":"10.1016\/j.media.2023.102967"},{"key":"16_CR21","doi-asserted-by":"publisher","unstructured":"Pinaya, W.H., et al.: Fast unsupervised brain anomaly detection and segmentation with diffusion models. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 705\u2013714. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_67","DOI":"10.1007\/978-3-031-16452-1_67"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Talebi, H., Milanfar, P.: Learning to resize images for computer vision tasks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 497\u2013506 (2021)","DOI":"10.1109\/ICCV48922.2021.00055"},{"key":"16_CR23","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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"issue":"4","key":"16_CR24","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Hu, X., Naiel, M.A., Wong, A., Lamm, M., Fieguth, P., 2019. RUNet: a robust UNet architecture for image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","DOI":"10.1109\/CVPRW.2019.00073"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Gatidis, S., et al.: A whole-body FDG-PET\/CT dataset with manually annotated tumor lesions. Sci. Data 9(1), 601 (2022)","DOI":"10.1038\/s41597-022-01718-3"},{"key":"16_CR27","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: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"16_CR28","unstructured":"Chen, S., Ma, K., Zheng, Y.: Med3D: transfer learning for 3D medical image analysis. arXiv preprint arXiv:1904.00625 (2019)"},{"key":"16_CR29","unstructured":"ADNI: Alzheimer\u2019s disease neuroimaging initiative. http:\/\/adni.loni.usc.edu\/"}],"container-title":["Lecture Notes in Computer Science","Deep Generative Models"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05472-2_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T09:25:26Z","timestamp":1758705926000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05472-2_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,25]]},"ISBN":["9783032054715","9783032054722"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05472-2_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,25]]},"assertion":[{"value":"25 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DGM4MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Deep Generative Models","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":"23 September 2025","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":"dgm4miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dgm4miccai.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}