{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T18:33:16Z","timestamp":1769884396693,"version":"3.49.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031720826","type":"print"},{"value":"9783031720833","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\/978-3-031-72083-3_50","type":"book-chapter","created":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T18:01:42Z","timestamp":1728842502000},"page":"535-545","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["URCDM: Ultra-Resolution Image Synthesis in\u00a0Histopathology"],"prefix":"10.1007","author":[{"given":"Sarah","family":"Cechnicka","sequence":"first","affiliation":[]},{"given":"James","family":"Ball","sequence":"additional","affiliation":[]},{"given":"Matthew","family":"Baugh","sequence":"additional","affiliation":[]},{"given":"Hadrien","family":"Reynaud","sequence":"additional","affiliation":[]},{"given":"Naomi","family":"Simmonds","sequence":"additional","affiliation":[]},{"given":"Andrew P. T.","family":"Smith","sequence":"additional","affiliation":[]},{"given":"Catherine","family":"Horsfield","sequence":"additional","affiliation":[]},{"given":"Candice","family":"Roufosse","sequence":"additional","affiliation":[]},{"given":"Bernhard","family":"Kainz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"50_CR1","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-031-45857-6_7","volume-title":"Domain Adaptation and Representation Transfer","author":"S Cechnicka","year":"2024","unstructured":"Cechnicka, S., Ball, J., Reynaud, H., Arthurs, C., Roufosse, C., Kainz, B.: Realistic data enrichment for robust image segmentation in histopathology. In: Koch, L., Cardoso, M.J., Ferrante, E., Kamnitsas, K., Islam, M., Jiang, M., Rieke, N., Tsaftaris, S.A., Yang, D. (eds.) Domain Adaptation and Representation Transfer. pp. 63\u201372. Springer Nature Switzerland, Cham (2024)"},{"key":"50_CR2","doi-asserted-by":"crossref","unstructured":"Chai, L., Gharbi, M., Shechtman, E., Isola, P., Zhang, R.: Any-resolution training for high-resolution image synthesis. In: European Conference on Computer Vision (2022)","DOI":"10.1007\/978-3-031-19787-1_10"},{"key":"50_CR3","doi-asserted-by":"crossref","unstructured":"Cheng, Y.C., Lin, C.H., Lee, H.Y., Ren, J., Tulyakov, S., Yang, M.H.: Inout: Diverse image outpainting via gan inversion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 11431\u201311440 (2022)","DOI":"10.1109\/CVPR52688.2022.01114"},{"key":"50_CR4","doi-asserted-by":"publisher","unstructured":"Ciga, O., Xu, T., Nofech-Mozes, S., Noy, S., Lu, F.I., Martel, A.L.: Overcoming the limitations of patch-based learning to detect cancer in whole slide images. Scientific Reports | 11, \u00a08894 (123). https:\/\/doi.org\/10.1038\/s41598-021-88494-z","DOI":"10.1038\/s41598-021-88494-z"},{"key":"50_CR5","unstructured":"Etten, A.V.: You only look twice: Rapid multi-scale object detection in satellite imagery (2018)"},{"issue":"11","key":"50_CR6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Communications of the ACM 63(11), 139\u2013144 (2020)","journal-title":"Communications of the ACM"},{"key":"50_CR7","doi-asserted-by":"crossref","unstructured":"Gupta, L., Klinkhammer, B.M., Boor, P., Merhof, D., Gadermayr, M.: Gan-based image enrichment in digital pathology boosts segmentation accuracy. In: MICCAI 2019, Part I 22. pp. 631\u2013639. Springer (2019)","DOI":"10.1007\/978-3-030-32239-7_70"},{"key":"50_CR8","doi-asserted-by":"publisher","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 (2017). https:\/\/doi.org\/10.48550\/ARXIV.1706.08500, https:\/\/arxiv.org\/abs\/1706.08500","DOI":"10.48550\/ARXIV.1706.08500"},{"key":"50_CR9","doi-asserted-by":"publisher","unstructured":"Jose, L., Liu, S., Russo, C., Nadort, A., Di Ieva, A.: Generative adversarial networks in digital pathology and histopathological image processing: A review. Journal of Pathology Informatics 12(1), \u00a043 (2021). https:\/\/doi.org\/10.4103\/jpi.jpi_103_20, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2153353922001651","DOI":"10.4103\/jpi.jpi_103_20"},{"key":"50_CR10","unstructured":"Karras, T., Aittala, M., Laine, S., H\u00e4rk\u00f6nen, E., Hellsten, J., Lehtinen, J., Aila, T.: Alias-free generative adversarial networks. In: Proc. NeurIPS (2021)"},{"key":"50_CR11","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"50_CR12","unstructured":"Kynk\u00e4\u00e4nniemi, T., Karras, T., Laine, S., Lehtinen, J., Aila, T.: Improved precision and recall metric for assessing generative models. CoRR abs\/1904.06991 (2019)"},{"key":"50_CR13","doi-asserted-by":"publisher","unstructured":"Laak, J., Litjens, G., Ciompi, F.: Deep learning in histopathology: the path to the clinic. Nature Medicine (2021). https:\/\/doi.org\/10.1038\/s41591-021-01343-4, https:\/\/doi.org\/10.1038\/s41591-021-01343-4","DOI":"10.1038\/s41591-021-01343-4"},{"key":"50_CR14","doi-asserted-by":"crossref","unstructured":"Lin, Y., Wang, Z., Cheng, K.T., Chen, H.: Insmix: Towards realistic generative data augmentation for nuclei instance segmentation. In: MICCAI 2022, Part II. pp. 140\u2013149. Springer (2022)","DOI":"10.1007\/978-3-031-16434-7_14"},{"key":"50_CR15","doi-asserted-by":"publisher","unstructured":"Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., Thomas, N.E.: A method for normalizing histology slides for quantitative analysis. In: Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28 - July 1, 2009. pp. 1107\u20131110. IEEE (2009). https:\/\/doi.org\/10.1109\/ISBI.2009.5193250, https:\/\/drive.google.com\/file\/d\/1eZGi1wUdyxVOYADXUbxZiVtajlztSnGL","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"50_CR16","doi-asserted-by":"crossref","unstructured":"Meng, C., Rombach, R., Gao, R., Kingma, D.P., Ermon, S., Ho, J., Salimans, T.: On distillation of guided diffusion models (2023)","DOI":"10.1109\/CVPR52729.2023.01374"},{"key":"50_CR17","doi-asserted-by":"crossref","unstructured":"Moghadam, P.A., Dalen, S.V., Martin, K.C., Lennerz, J., Yip, S., Farahani, H., Bashashati, A.: A morphology focused diffusion probabilistic model for synthesis of histopathology images. In: 2023 IEEE conference on computer vision and pattern recognition (2023)","DOI":"10.1109\/WACV56688.2023.00204"},{"key":"50_CR18","unstructured":"Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, Alec, e.a.: Zero-Shot Text-to-Image Generation (February 2021), arXiv:2102.12092"},{"key":"50_CR19","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/978-3-031-43999-5_14","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2023","author":"H Reynaud","year":"2023","unstructured":"Reynaud, H., Qiao, M., Dombrowski, M., Day, T., Razavi, R., Gomez, A., Leeson, P., Kainz, B.: Feature-conditioned cascaded video diffusion models for precise echocardiogram synthesis. In: Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T., Taylor, R. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023. pp. 142\u2013152. Springer Nature Switzerland, Cham (2023)"},{"key":"50_CR20","doi-asserted-by":"publisher","unstructured":"RL, G., AP, H., V, F., HE, V., DR, L., WA, K., LM., S.: Toward a shared vision for cancer genomic data. N Engl J Med. (2016). https:\/\/doi.org\/10.1056\/NEJMp1607591","DOI":"10.1056\/NEJMp1607591"},{"key":"50_CR21","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-Resolution Image Synthesis with Latent Diffusion Models (April 2022), arXiv:2112.10752","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"50_CR22","doi-asserted-by":"publisher","unstructured":"Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S.K.S., Ayan, B.K., Mahdavi, S.S., Lopes, R.G., Salimans, T., Ho, J., Fleet, D.J., Norouzi, M.: Photorealistic text-to-image diffusion models with deep language understanding (2022). https:\/\/doi.org\/10.48550\/ARXIV.2205.11487, https:\/\/arxiv.org\/abs\/2205.11487","DOI":"10.48550\/ARXIV.2205.11487"},{"key":"50_CR23","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1007\/978-3-031-43987-2_76","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2023","author":"A Shrivastava","year":"2023","unstructured":"Shrivastava, A., Fletcher, P.T.: Nasdm: Nuclei-aware semantic histopathology image generation using diffusion models. In: Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T., Taylor, R. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023. pp. 786\u2013796. Springer Nature Switzerland, Cham (2023)"},{"key":"50_CR24","unstructured":"stanford Song, Y.S., Sohl-Dickstein, J., Brain, G., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. ICLR (2021)"},{"key":"50_CR25","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-031-16434-7_2","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2022","author":"N Wagner","year":"2022","unstructured":"Wagner, N., Fuchs, M., Tolkach, Y., Mukhopadhyay, A.: Federated stain normalization for computational pathology. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022. pp. 14\u201323. Springer Nature Switzerland, Cham (2022)"},{"issue":"2017","key":"50_CR26","first-page":"1","volume":"11","author":"J Wang","year":"2017","unstructured":"Wang, J., Perez, L., et\u00a0al.: The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks Vis. Recognit 11(2017), \u00a01\u20138 (2017)","journal-title":"Convolutional Neural Networks Vis. Recognit"},{"key":"50_CR27","unstructured":"Wang, P.: lucidrains\/imagen-pytorch: Implementation of Imagen, Google\u2019s Text-to-Image Neural Network, in Pytorch \u2014 github.com. https:\/\/github.com\/lucidrains\/imagen-pytorch (2022), [Accessed 12-Nov-2022]"},{"key":"50_CR28","doi-asserted-by":"publisher","unstructured":"Weinstein, J.N., Collisson, E.A., Mills, G.B., Shaw, K.R.M., Ozenberger, B.A., Ellrott, K., Shmulevich, I., Sander, C., Stuart, J.M.: The cancer genome atlas pan-cancer analysis project. Nature Publishing Group (2013). https:\/\/doi.org\/10.1038\/ng.2764, http:\/\/www.cancergenome.nih.gov\/.","DOI":"10.1038\/ng.2764"},{"key":"50_CR29","doi-asserted-by":"publisher","unstructured":"Wu, B., Moeckel, G.: Application of digital pathology and machine learning in the liver, kidney and lung diseases. Journal of Pathology Informatics 14, 100184 (2023). https:\/\/doi.org\/10.1016\/j.jpi.2022.100184","DOI":"10.1016\/j.jpi.2022.100184"}],"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\/978-3-031-72083-3_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T18:12:04Z","timestamp":1728843124000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72083-3_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031720826","9783031720833"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72083-3_50","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":"14 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 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":"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"}}]}}