{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:10:21Z","timestamp":1758759021860,"version":"3.44.0"},"publisher-location":"Cham","reference-count":26,"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_32","type":"book-chapter","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T09:25:11Z","timestamp":1758705911000},"page":"330-340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MedIL: Generating Arbitrary-Resolution Medical Images with\u00a0Implicit Latent Spaces"],"prefix":"10.1007","author":[{"given":"Tyler","family":"Spears","sequence":"first","affiliation":[]},{"given":"Shen","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yinzhu","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Aman","family":"Shrivastava","sequence":"additional","affiliation":[]},{"given":"P. Thomas","family":"Fletcher","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"key":"32_CR1","unstructured":"Armato\u00a0III, S.G., et al.: Data From LIDC-IDRI (2015)"},{"key":"32_CR2","unstructured":"Cardoso, M.J., et al.: MONAI: An open-source framework for deep learning in healthcare (Nov 2022)"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Chai, L., Gharbi, M., Shechtman, E., Isola, P., Zhang, R.: Any-resolution training for\u00a0high-resolution image synthesis. In: European Conference on Computer Vision \u2013 ECCV 2022, pp. 170\u2013188 (2022)","DOI":"10.1007\/978-3-031-19787-1_10"},{"key":"32_CR4","unstructured":"Chen, S., Ma, K., Zheng, Y.: Med3D: Transfer Learning for 3D Medical Image Analysis (Jul 2019)"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, S., Wang, X.: Learning continuous image representation with local implicit image function. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8628\u20138638 (2021)","DOI":"10.1109\/CVPR46437.2021.00852"},{"key":"32_CR6","unstructured":"Dorjsembe, Z., Odonchimed, S., Xiao, F.: Three-dimensional medical image synthesis with denoising diffusion probabilistic models. In: Medical Imaging with Deep Learning (Apr 2022)"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Evans, A., Collins, D., Mills, S., Brown, E., Kelly, R., Peters, T.: 3D statistical neuroanatomical models from 305 MRI volumes. In: 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, pp. 1813\u20131817 vol.3 (Oct 1993)","DOI":"10.1109\/NSSMIC.1993.373602"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Friedrich, P., Wolleb, J., Bieder, F., Durrer, A., Cattin, P.C.: WDM: 3D wavelet diffusion models for\u00a0high-resolution medical image synthesis. In: Deep Generative Models, pp. 11\u201321 (2025)","DOI":"10.1007\/978-3-031-72744-3_2"},{"key":"32_CR9","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: Neural Information Processing Systems, p.\u00a012 (2017)"},{"key":"32_CR10","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol.\u00a033, pp. 6840\u20136851 (2020)"},{"issue":"1","key":"32_CR11","doi-asserted-by":"publisher","first-page":"7303","DOI":"10.1038\/s41598-023-34341-2","volume":"13","author":"F Khader","year":"2023","unstructured":"Khader, F., et al.: Denoising diffusion probabilistic models for 3d medical image generation. Sci. Rep. 13(1), 7303 (2023)","journal-title":"Sci. Rep."},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Lee, J., Jin, K.H.: Local texture estimator for implicit representation function. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1929\u20131938 (2022)","DOI":"10.1109\/CVPR52688.2022.00197"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Mei, X., et al.: RadImageNet: an open radiologic deep learning research dataset for effective transfer learning. Radiol.: Artif. Intell. 4(5), e210315 (Sep 2022)","DOI":"10.1148\/ryai.210315"},{"issue":"1","key":"32_CR14","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99\u2013106 (2021)","journal-title":"Commun. ACM"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Ntavelis, E., Shahbazi, M., Kastanis, I., Timofte, R., Danelljan, M., Van\u00a0Gool, L.: Arbitrary-scale image synthesis. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11523\u201311532. IEEE (Jun 2022)","DOI":"10.1109\/CVPR52688.2022.01124"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Pech-Pacheco, J., Cristobal, G., Chamorro-Martinez, J., Fernandez-Valdivia, J.: Diatom autofocusing in brightfield microscopy: a comparative study. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, vol.\u00a03, pp. 314\u2013317 vol.3 (Sep 2000)","DOI":"10.1109\/ICPR.2000.903548"},{"key":"32_CR17","doi-asserted-by":"crossref","unstructured":"Peng, W., et al.: Metadata-conditioned generative models to synthesize anatomically-plausible 3D brain MRIs. Med. Image Anal. 98, 103325 (2024)","DOI":"10.1016\/j.media.2024.103325"},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H.L., et al.: Brain imaging generation with\u00a0latent diffusion models. In: Deep Generative Models, pp. 117\u2013126 (2022)","DOI":"10.1007\/978-3-031-18576-2_12"},{"key":"32_CR19","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":"32_CR20","doi-asserted-by":"publisher","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","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"32_CR21","unstructured":"Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. In: Advances in Neural Information Processing Systems. vol.\u00a033, pp. 7462\u20137473 (2020)"},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Skorokhodov, I., Ignatyev, S., Elhoseiny, M.: Adversarial generation of continuous images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10753\u201310764 (2021)","DOI":"10.1109\/CVPR46437.2021.01061"},{"key":"32_CR23","unstructured":"Tancik, M., et al.: Fourier features let networks learn high frequency functions in low dimensional domains. In: Advances in Neural Information Processing Systems, vol.\u00a033, pp. 7537\u20137547 (2020)"},{"key":"32_CR24","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","volume":"80","author":"DC Essen","year":"2013","unstructured":"Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E.J., Yacoub, E., Ugurbil, K.: The wu-minn human connectome project: an overview. Neuroimage 80, 62\u201379 (2013)","journal-title":"Neuroimage"},{"key":"32_CR25","unstructured":"Wang, Z., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image quality assessment. In: The Thirty-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, vol.\u00a02, pp. 1398\u20131402 Vol.2 (Nov 2003)"},{"issue":"4","key":"32_CR26","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., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."}],"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_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T09:25:32Z","timestamp":1758705932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05472-2_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,25]]},"ISBN":["9783032054715","9783032054722"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05472-2_32","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":"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":"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"}}]}}