{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T03:55:10Z","timestamp":1774670110053,"version":"3.50.1"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031917202","type":"print"},{"value":"9783031917219","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-91721-9_3","type":"book-chapter","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T05:03:56Z","timestamp":1748495036000},"page":"34-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Can Virtual Staining for\u00a0High-Throughput Screening Generalize?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1408-1493","authenticated-orcid":false,"given":"Samuel","family":"Tonks","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5151-1893","authenticated-orcid":false,"given":"Cuong","family":"Nguyen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7708-7699","authenticated-orcid":false,"given":"Steve","family":"Hood","sequence":"additional","affiliation":[]},{"given":"Ryan","family":"Musso","sequence":"additional","affiliation":[]},{"given":"Ceridwen","family":"Hopely","sequence":"additional","affiliation":[]},{"given":"Steve","family":"Titus","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3235-0457","authenticated-orcid":false,"given":"Minh","family":"Doan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6755-0299","authenticated-orcid":false,"given":"Iain","family":"Styles","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7778-7169","authenticated-orcid":false,"given":"Alexander","family":"Krull","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"issue":"10","key":"3_CR1","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1038\/s41592-021-01252-x","volume":"18","author":"\u017d Avsec","year":"2021","unstructured":"Avsec, \u017d, et al.: Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18(10), 1196\u20131203 (2021)","journal-title":"Nat. Methods"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Caicedo, J.C.e.a.: Nucleus segmentation across imaging experiments: the 2018 data science bowl. Nat. Methods 16(12), 1247\u20131253 (2019)","DOI":"10.1038\/s41592-019-0612-7"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Carpenter, A.E.e.a.: CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, 1\u201311 (2006)","DOI":"10.1186\/gb-2006-7-10-r100"},{"key":"3_CR4","unstructured":"Chandrasekaran, S.N.e.a.: Jump cell painting dataset: Morphological impact of 136,000 chemical and genetic perturbations. BioRxiv (2023). https:\/\/www.biorxiv.org\/content\/10.1101\/2023.03.23.534023v2"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Christiansen, E.M.e.a.: In silico labeling: predicting fluorescent labels in unlabeled images. Cell 173(3), 792\u2013803 (2018)","DOI":"10.1016\/j.cell.2018.03.040"},{"key":"3_CR6","unstructured":"Cobbe, K., Klimov, O., Hesse, C., Kim, T., Schulman, J.: Quantifying generalization in reinforcement learning. In: International Conference on Machine Learning, pp. 1282\u20131289. PMLR (2019)"},{"key":"3_CR7","unstructured":"Cooper, G.M.: The cell: A molecular approach. 2nd edition. Sunderland (MA): Sinauer Associates; 2000.Signaling Molecules and Their Receptors (2000). https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK9924\/"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Cross-Zamirski, J.O., Anand, P., Williams, G., Mouchet, E., Wang, Y., Sch\u00f6nlieb, C.B.: Class-guided image-to-image diffusion: Cell painting from brightfield images with class labels. arXiv preprint arXiv:2303.08863 (2023)","DOI":"10.1109\/ICCVW60793.2023.00411"},{"issue":"1","key":"3_CR9","doi-asserted-by":"publisher","first-page":"10001","DOI":"10.1038\/s41598-022-12914-x","volume":"12","author":"JO Cross-Zamirski","year":"2022","unstructured":"Cross-Zamirski, J.O., Mouchet, E., Williams, G., Sch\u00f6nlieb, C.B., Turkki, R., Wang, Y.: Label-free prediction of cell painting from brightfield images. Sci. Rep. 12(1), 10001 (2022)","journal-title":"Sci. Rep."},{"issue":"4","key":"3_CR10","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1038\/s41416-020-01122-x","volume":"124","author":"A Echle","year":"2021","unstructured":"Echle, A., Rindtorff, N.T., Brinker, T.J., Luedde, T., Pearson, A.T., Kather, J.N.: Deep learning in cancer pathology: a new generation of clinical biomarkers. Br. J. Cancer 124(4), 686\u2013696 (2021)","journal-title":"Br. J. Cancer"},{"issue":"1","key":"3_CR11","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.mpsur.2012.10.018","volume":"31","author":"H Ellis","year":"2013","unstructured":"Ellis, H., Mahadevan, V.: Anatomy and physiology of the breast. Surgery (Oxford) 31(1), 11\u201314 (2013)","journal-title":"Surgery (Oxford)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Faragallah, O.S., et al.: A comprehensive survey analysis for present solutions of medical image fusion and future directions. IEEE Access 9, 11358\u201311371 (2020)","DOI":"10.1109\/ACCESS.2020.3048315"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Gupta, A., et al.: Is brightfield all you need for mechanism of action prediction? bioRxiv, pp. 2022\u201310 (2022)","DOI":"10.1101\/2022.10.12.511869"},{"issue":"7","key":"3_CR14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1011323","volume":"19","author":"PJ Harrison","year":"2023","unstructured":"Harrison, P.J., et al.: Evaluating the utility of brightfield image data for mechanism of action prediction. PLoS Comput. Biol. 19(7), e1011323 (2023)","journal-title":"PLoS Comput. Biol."},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Imboden, S., Liu, X., Payne, M.C., Hsieh, C.J., Lin, N.Y.: Trustworthy in silico cell labeling via ensemble-based image translation. Biophys. Rep. 3(4) (2023)","DOI":"10.1016\/j.bpr.2023.100133"},{"key":"3_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":"3_CR17","doi-asserted-by":"crossref","unstructured":"Jaccard, P.: The distribution of the flora in the alpine zone. 1. New Phytol. 11(2), 37\u201350 (1912)","DOI":"10.1111\/j.1469-8137.1912.tb05611.x"},{"issue":"Suppl","key":"3_CR18","doi-asserted-by":"publisher","first-page":"S64","DOI":"10.1007\/BF00684866","volume":"34","author":"K Kobayashi","year":"1994","unstructured":"Kobayashi, K., Ratain, M.J.: Pharmacodynamics and long-term toxicity of etoposide. Cancer Chemother. Pharmacol. 34(Suppl), S64-8 (1994)","journal-title":"Cancer Chemother. Pharmacol."},{"issue":"5","key":"3_CR19","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1038\/s41591-021-01343-4","volume":"27","author":"J Van der Laak","year":"2021","unstructured":"Van der Laak, J., Litjens, G., Ciompi, F.: Deep learning in histopathology: the path to the clinic. Nat. Med. 27(5), 775\u2013784 (2021)","journal-title":"Nat. Med."},{"key":"3_CR20","unstructured":"Levine, S., Kumar, A., Tucker, G., Fu, J.: Offline reinforcement learning: Tutorial, review, and perspectives on open problems. arXiv preprint arXiv:2005.01643 (2020)"},{"key":"3_CR21","first-page":"20612","volume":"33","author":"Y Luo","year":"2020","unstructured":"Luo, Y., Liu, P., Guan, T., Yu, J., Yang, Y.: Adversarial style mining for one-shot unsupervised domain adaptation. Adv. Neural. Inf. Process. Syst. 33, 20612\u201320623 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR22","unstructured":"Mediratta, I., You, Q., Jiang, M., Raileanu, R.: The generalization gap in offline reinforcement learning. arXiv preprint arXiv:2312.05742 (2023)"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Murez, Z., Kolouri, S., Kriegman, D., Ramamoorthi, R., Kim, K.: Image to image translation for domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4500\u20134509 (2018)","DOI":"10.1109\/CVPR.2018.00473"},{"issue":"11","key":"3_CR24","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1038\/s41592-018-0111-2","volume":"15","author":"C Ounkomol","year":"2018","unstructured":"Ounkomol, C., Seshamani, S., Maleckar, M.M., Collman, F., Johnson, G.R.: Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat. Methods 15(11), 917\u2013920 (2018)","journal-title":"Nat. Methods"},{"issue":"12","key":"3_CR25","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1038\/s41566-022-01096-7","volume":"16","author":"D Pirone","year":"2022","unstructured":"Pirone, D., et al.: Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry. Nat. Photonics 16(12), 851\u2013859 (2022)","journal-title":"Nat. Photonics"},{"issue":"1","key":"3_CR26","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/0304-3835(96)04346-7","volume":"107","author":"L Qiao","year":"1996","unstructured":"Qiao, L., et al.: Staurosporine inhibits the proliferation, alters the cell cycle distribution and induces apoptosis in ht-29 human colon adenocarcinoma cells. Cancer Lett. 107(1), 83\u20139 (1996)","journal-title":"Cancer Lett."},{"key":"3_CR27","unstructured":"Saabas, A.: Selecting good features - part iii: Random forests (2014). https:\/\/blog.datadive.net\/selecting-good-features-part-iii-random-forests\/, data Science Central Blog"},{"key":"3_CR28","unstructured":"Sasaki, Y.e.a.: The truth of the f-measure. Teaching, Tutorial Materials, Version: 26th October 1(5), 1\u20135 (2007)"},{"issue":"10","key":"3_CR29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0007497","volume":"4","author":"J Selinummi","year":"2009","unstructured":"Selinummi, J., et al.: Bright field microscopy as an alternative to whole cell fluorescence in automated analysis of macrophage images. PLoS ONE 4(10), e7497 (2009)","journal-title":"PLoS ONE"},{"issue":"1","key":"3_CR30","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","volume":"18","author":"C Stringer","year":"2021","unstructured":"Stringer, C., Wang, T., Michaelos, M., Pachitariu, M.: Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18(1), 100\u2013106 (2021)","journal-title":"Nat. Methods"},{"issue":"1","key":"3_CR31","doi-asserted-by":"publisher","first-page":"427","DOI":"10.3390\/ijms13010427","volume":"13","author":"P Szyma\u0144ski","year":"2011","unstructured":"Szyma\u0144ski, P., Markowicz, M., Mikiciuk-Olasik, E.: Adaptation of high-throughput screening in drug discovery-toxicological screening tests. Int. J. Mol. Sci. 13(1), 427\u2013452 (2011)","journal-title":"Int. J. Mol. Sci."},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Tonks, S., et al.: Evaluation of virtual staining for high-throughput screenings. In: 20th IEEE International Symposium on Biomedical Imaging. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230501"},{"key":"3_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1007\/978-3-030-87199-4_58","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"U Upadhyay","year":"2021","unstructured":"Upadhyay, U., Chen, Y., Hepp, T., Gatidis, S., Akata, Z.: Uncertainty-guided progressive GANs for medical image translation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 614\u2013624. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_58"},{"issue":"9","key":"3_CR34","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1038\/s41591-022-01905-0","volume":"28","author":"SJ Wagner","year":"2022","unstructured":"Wagner, S.J., et al.: Make deep learning algorithms in computational pathology more reproducible and reusable. Nat. Med. 28(9), 1744\u20131746 (2022)","journal-title":"Nat. Med."},{"key":"3_CR35","doi-asserted-by":"crossref","unstructured":"Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8798\u20138807 (2018)","DOI":"10.1109\/CVPR.2018.00917"},{"issue":"4","key":"3_CR36","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."},{"issue":"10","key":"3_CR37","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0258546","volume":"16","author":"H Wieslander","year":"2021","unstructured":"Wieslander, H., Gupta, A., Bergman, E., Hallstr\u00f6m, E., Harrison, P.J.: Learning to see colours: biologically relevant virtual staining for adipocyte cell images. PLoS ONE 16(10), e0258546 (2021)","journal-title":"PLoS ONE"},{"key":"3_CR38","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/0041-008X(65)90081-5","volume":"7","author":"JE Wilson","year":"1965","unstructured":"Wilson, J.E., Brown, D.E., Timmens, E.K.: A toxicologic study of dimethyl sulfoxide. Toxicol. Appl. Pharmacol. 7, 104\u201312 (1965)","journal-title":"Toxicol. Appl. Pharmacol."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91721-9_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T05:04:08Z","timestamp":1748495048000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91721-9_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031917202","9783031917219"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91721-9_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}