{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T06:19:09Z","timestamp":1758349149601,"version":"3.44.0"},"publisher-location":"Cham","reference-count":28,"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_42","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:39:58Z","timestamp":1758260398000},"page":"442-452","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pathology Image Compression with\u00a0Pre-trained Autoencoders"],"prefix":"10.1007","author":[{"given":"Srikar","family":"Yellapragada","sequence":"first","affiliation":[]},{"given":"Alexandros","family":"Graikos","sequence":"additional","affiliation":[]},{"given":"Kostas","family":"Triaridis","sequence":"additional","affiliation":[]},{"given":"Zilinghan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tarak Nath","family":"Nandi","sequence":"additional","affiliation":[]},{"given":"Ravi K.","family":"Madduri","sequence":"additional","affiliation":[]},{"given":"Prateek","family":"Prasanna","sequence":"additional","affiliation":[]},{"given":"Joel","family":"Saltz","sequence":"additional","affiliation":[]},{"given":"Dimitris","family":"Samaras","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"issue":"18","key":"42_CR1","doi-asserted-by":"publisher","first-page":"3461","DOI":"10.1093\/bioinformatics\/btz083","volume":"35","author":"M Amgad","year":"2019","unstructured":"Amgad, M., et al.: Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics 35(18), 3461\u20133467 (2019)","journal-title":"Bioinformatics"},{"key":"42_CR2","unstructured":"Ball\u00e9, J., Laparra, V., Simoncelli, E.P.: End-to-end optimized image compression. In: 5th International Conference on Learning Representations, ICLR 2017 (2017)"},{"key":"42_CR3","doi-asserted-by":"crossref","unstructured":"Cancer Genome Atlas Research\u00a0Network, J., et\u00a0al.: The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45(10), 1113\u20131120 (2013)","DOI":"10.1038\/ng.2764"},{"key":"42_CR4","unstructured":"Chen, J., et al.: Deep compression autoencoder for efficient high-resolution diffusion models. arXiv preprint arXiv:2410.10733 (2024)"},{"key":"42_CR5","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et\u00a0al.: Towards a general-purpose foundation model for computational pathology. Nat. Med. (2024)","DOI":"10.1038\/s41591-024-02857-3"},{"key":"42_CR6","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1200\/CCI.19.00068","volume":"4","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Janowczyk, A., Madabhushi, A.: Quantitative assessment of the effects of compression on deep learning in digital pathology image analysis. JCO Clin. Cancer Inform. 4, 221\u2013233 (2020)","journal-title":"JCO Clin. Cancer Inform."},{"key":"42_CR7","unstructured":"Esser, P., et\u00a0al.: Scaling rectified flow transformers for high-resolution image synthesis. In: Forty-First International Conference on Machine Learning (2024)"},{"key":"42_CR8","doi-asserted-by":"publisher","unstructured":"Filiot, A., et al.: Scaling self-supervised learning for histopathology with masked image modeling. medRxiv (2023). https:\/\/doi.org\/10.1101\/2023.07.21.23292757, https:\/\/www.medrxiv.org\/content\/early\/2023\/07\/26\/2023.07.21.23292757","DOI":"10.1101\/2023.07.21.23292757"},{"key":"42_CR9","unstructured":"Filiot, A., Jacob, P., Mac\u00a0Kain, A., Saillard, C.: Phikon-v2, a large and public feature extractor for biomarker prediction. arXiv preprint arXiv:2409.09173 (2024)"},{"key":"42_CR10","doi-asserted-by":"crossref","unstructured":"Fischer, M., Maier-Hein, K.: Learned image compression for he-stained histopathological images via stain deconvolution. In: Medical Optical Imaging and Virtual Microscopy Image Analysis: Second International Workshop, MOVI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings, p.\u00a097. Springer Nature (2024)","DOI":"10.1007\/978-3-031-77786-8_10"},{"key":"42_CR11","doi-asserted-by":"crossref","unstructured":"Fischer, M., et\u00a0al.: Enhanced diagnostic fidelity in pathology whole slide image compression via deep learning. In: International Workshop on Machine Learning in Medical Imaging, pp. 427\u2013436. Springer (2023)","DOI":"10.1007\/978-3-031-45676-3_43"},{"key":"42_CR12","doi-asserted-by":"crossref","unstructured":"Fischer, M., et\u00a0al.: Unlocking the potential of digital pathology: novel baselines for compression. J. Pathol. Inform., 100421 (2025)","DOI":"10.1016\/j.jpi.2025.100421"},{"key":"42_CR13","doi-asserted-by":"publisher","unstructured":"Graham, S., et al.: MILD-Net: minimal information loss dilated network for gland instance segmentation in colon histology images. Med. Image Anal. 52, 199\u2013211 (2019). https:\/\/doi.org\/10.1016\/j.media.2018.12.001, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841518306030","DOI":"10.1016\/j.media.2018.12.001"},{"key":"42_CR14","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"42_CR15","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127\u20132136. PMLR (2018)"},{"key":"42_CR16","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"42_CR17","doi-asserted-by":"publisher","unstructured":"Kather, J.N., Halama, N., Marx, A.: 100,000 histological images of human colorectal cancer and healthy tissue (2018). https:\/\/doi.org\/10.5281\/zenodo.1214456","DOI":"10.5281\/zenodo.1214456"},{"key":"42_CR18","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014). http:\/\/arxiv.org\/abs\/1312.6114"},{"key":"42_CR19","doi-asserted-by":"crossref","unstructured":"Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318\u201314328 (2021)","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"42_CR20","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1038\/s41591-024-02856-4","volume":"30","author":"MY Lu","year":"2024","unstructured":"Lu, M.Y., et al.: A visual-language foundation model for computational pathology. Nat. Med. 30, 863\u2013874 (2024)","journal-title":"Nat. Med."},{"key":"42_CR21","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"},{"issue":"9","key":"42_CR22","doi-asserted-by":"publisher","first-page":"1874","DOI":"10.1093\/jamia\/ocab085","volume":"28","author":"PJ Sch\u00fcffler","year":"2021","unstructured":"Sch\u00fcffler, P.J., et al.: Integrated digital pathology at scale: a solution for clinical diagnostics and cancer research at a large academic medical center. J. Am. Med. Inform. Assoc. 28(9), 1874\u20131884 (2021)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"2","key":"42_CR23","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1109\/TPAMI.2019.2936841","volume":"43","author":"D Tellez","year":"2019","unstructured":"Tellez, D., Litjens, G., Van der Laak, J., Ciompi, F.: Neural image compression for gigapixel histopathology image analysis. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 567\u2013578 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"42_CR24","unstructured":"Theis, L., Shi, W., Cunningham, A., Husz\u00e1r, F.: Lossy image compression with compressive autoencoders. In: International Conference on Learning Representations (2017)"},{"issue":"8015","key":"42_CR25","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1038\/s41586-024-07441-w","volume":"630","author":"H Xu","year":"2024","unstructured":"Xu, H., et al.: A whole-slide foundation model for digital pathology from real-world data. Nature 630(8015), 181\u2013188 (2024)","journal-title":"Nature"},{"key":"42_CR26","unstructured":"Xu, X., Kapse, S., Prasanna, P.: Histo-Diffusion: a diffusion super-resolution method for digital pathology with comprehensive quality assessment. arXiv preprint arXiv:2408.15218 (2024)"},{"key":"42_CR27","doi-asserted-by":"publisher","unstructured":"Zhang, J., et al.: SAM-Path: a segment anything model for semantic segmentation in digital pathology. In: Celebi, M.E., et al. International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 161\u2013170. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-47401-9_16","DOI":"10.1007\/978-3-031-47401-9_16"},{"key":"42_CR28","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, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"}],"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_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:40:10Z","timestamp":1758260410000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04937-7_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049360","9783032049377"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04937-7_42","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"}}]}}