{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:04:35Z","timestamp":1784642675365,"version":"3.55.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032191045","type":"print"},{"value":"9783032191052","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-19105-2_24","type":"book-chapter","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:10:50Z","timestamp":1778364650000},"page":"336-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sex Prediction from\u00a0Polish Ethnicity Fundus Images Using Foundation Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8189-5273","authenticated-orcid":false,"given":"Pawe\u0142","family":"Niedzi\u00f3\u0142ka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4224-6709","authenticated-orcid":false,"given":"Pawe\u0142","family":"Zyblewski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3724-2391","authenticated-orcid":false,"given":"Andrzej","family":"Grzybowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0146-4205","authenticated-orcid":false,"given":"Micha\u0142","family":"Wo\u017aniak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5202-1968","authenticated-orcid":false,"given":"\u0141ukasz","family":"Lisowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5977-0745","authenticated-orcid":false,"given":"Marlena","family":"Dubat\u00f3wka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9465-2581","authenticated-orcid":false,"given":"Karol","family":"Kami\u0144ski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Achille, A., et al.: Task2vec: task embedding for meta-learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6430\u20136439 (2019)","DOI":"10.1109\/ICCV.2019.00653"},{"key":"24_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100377","volume":"20","author":"W Alyoubi","year":"2020","unstructured":"Alyoubi, W., et al.: Diabetic retinopathy detection through deep learning techniques: a review. Inform. Med. Unlocked 20, 100377 (2020)","journal-title":"Inform. Med. Unlocked"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Arun, N., et\u00a0al.: Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging. Radiol.: Artif. Intell. 3(6), e200267 (2021)","DOI":"10.1148\/ryai.2021200267"},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Bao, Y., et al.: An information-theoretic approach to transferability in task transfer learning. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 2309\u20132313. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803726"},{"issue":"2","key":"24_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/ima.70063","volume":"35","author":"M Butt","year":"2025","unstructured":"Butt, M., Awang Iskandar, D., Khan, M.A., Latif, G., Bashar, A.: Medcnet: a memory efficient approach for processing high-resolution fundus images for diabetic retinopathy classification using CNN. Int. J. Imaging Syst. Technol. 35(2), e70063 (2025)","journal-title":"Int. J. Imaging Syst. Technol."},{"issue":"6","key":"24_CR6","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1038\/s41551-023-01056-8","volume":"7","author":"RJ Chen","year":"2023","unstructured":"Chen, R.J., et al.: Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat. Biomed. Eng. 7(6), 719\u2013742 (2023)","journal-title":"Nat. Biomed. Eng."},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Chinta, S.V., et al.: AI-driven healthcare: a survey on ensuring fairness and mitigating bias. arXiv preprint arXiv:2407.19655 (2024)","DOI":"10.1371\/journal.pdig.0000864"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Cui, Y., Song, Y., Sun, C., Howard, A., Belongie, S.: Large scale fine-grained categorization and domain-specific transfer learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4109\u20134118 (2018)","DOI":"10.1109\/CVPR.2018.00432"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Dwivedi, K., Roig, G.: Representation similarity analysis for efficient task taxonomy & transfer learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12387\u201312396 (2019)","DOI":"10.1109\/CVPR.2019.01267"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Gulshan, V., et\u00a0al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402\u20132410 (2016)","DOI":"10.1001\/jama.2016.17216"},{"issue":"12","key":"24_CR12","doi-asserted-by":"publisher","first-page":"e60","DOI":"10.2105\/AJPH.2015.302903","volume":"105","author":"WJ Hall","year":"2015","unstructured":"Hall, W.J., et al.: Implicit racial\/ethnic bias among health care professionals and its influence on health care outcomes: a systematic review. Am. J. Public Health 105(12), e60\u2013e76 (2015)","journal-title":"Am. J. Public Health"},{"key":"24_CR13","unstructured":"Huang, L.K., Huang, J., Rong, Y., Yang, Q., Wei, Y.: Frustratingly easy transferability estimation. In: International Conference on Machine Learning, pp. 9201\u20139225. PMLR (2022)"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Khalifa, M., Albadawy, M.: AI in diagnostic imaging: revolutionising accuracy and efficiency. Comput. Methods Programs Biomed. Update 100146 (2024)","DOI":"10.1016\/j.cmpbup.2024.100146"},{"key":"24_CR15","doi-asserted-by":"publisher","unstructured":"Liu, S., Zhao, H., Huang, L., Ma, C., Wang, Q., Liu, L.: Vascular features around the optic disc in familial exudative vitreoretinopathy: findings and their relationship to disease severity. BMC Ophthalmol. 23 (2023). https:\/\/doi.org\/10.1186\/s12886-023-02884-7","DOI":"10.1186\/s12886-023-02884-7"},{"issue":"13","key":"24_CR16","doi-asserted-by":"publisher","first-page":"811","DOI":"10.3109\/08958378.2014.955932","volume":"26","author":"LD Maxim","year":"2014","unstructured":"Maxim, L.D., Niebo, R., Utell, M.J.: Screening tests: a review with examples. Inhalation Toxicol. 26(13), 811\u2013828 (2014)","journal-title":"Inhalation Toxicol."},{"key":"24_CR17","unstructured":"Nguyen, C., Hassner, T., Seeger, M., Archambeau, C.: Leep: a new measure to evaluate transferability of learned representations. In: International Conference on Machine Learning, pp. 7294\u20137305. PMLR (2020)"},{"issue":"2","key":"24_CR18","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/TAI.2021.3054609","volume":"1","author":"S Niu","year":"2021","unstructured":"Niu, S., Liu, Y., Wang, J., Song, H.: A decade survey of transfer learning (2010\u20132020). IEEE Trans. Artif. Intell. 1(2), 151\u2013166 (2021)","journal-title":"IEEE Trans. Artif. Intell."},{"issue":"6","key":"24_CR19","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1007\/s00592-017-0974-1","volume":"54","author":"PH Scanlon","year":"2017","unstructured":"Scanlon, P.H.: The English national screening programme for diabetic retinopathy 2003\u20132016. Acta Diabetol. 54(6), 515\u2013525 (2017)","journal-title":"Acta Diabetol."},{"key":"24_CR20","unstructured":"Settles, B.: Active learning literature survey (2009)"},{"key":"24_CR21","unstructured":"Shan, S., Cryan, J., Wenger, E., Zheng, H., Hanocka, R., Zhao, B.Y.: Glaze: protecting artists from style mimicry by text-to-image models. In: Proceedings of the 32nd USENIX Conference on Security Symposium, SEC 2023. USENIX Association, USA (2023)"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Shan, S., Ding, W., Passananti, J., Wu, S., Zheng, H., Zhao, B.Y.: Nightshade: prompt-specific poisoning attacks on text-to-image generative models (2024). https:\/\/arxiv.org\/abs\/2310.13828","DOI":"10.1109\/SP54263.2024.00207"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Song, J., et al.: Depara: deep attribution graph for deep knowledge transferability. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3922\u20133930 (2020)","DOI":"10.1109\/CVPR42600.2020.00398"},{"key":"24_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107219","volume":"104","author":"K Stapor","year":"2021","unstructured":"Stapor, K., Ksieniewicz, P., Garc\u00eda, S., Wo\u017aniak, M.: How to design the fair experimental classifier evaluation. Appl. Soft Comput. 104, 107219 (2021)","journal-title":"Appl. Soft Comput."},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Ting, D.S.W., et\u00a0al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama 318(22), 2211\u20132223 (2017)","DOI":"10.1001\/jama.2017.18152"},{"key":"24_CR26","doi-asserted-by":"crossref","unstructured":"Tran, A.T., Nguyen, C.V., Hassner, T.: Transferability and hardness of supervised classification tasks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1395\u20131405 (2019)","DOI":"10.1109\/ICCV.2019.00148"},{"issue":"1","key":"24_CR27","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TCDS.2020.3007453","volume":"14","author":"D Wu","year":"2020","unstructured":"Wu, D., Xu, Y., Lu, B.L.: Transfer learning for EEG-based brain-computer interfaces: a review of progress made since 2016. IEEE Trans. Cogn. Dev. Syst. 14(1), 4\u201319 (2020)","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"12","key":"24_CR28","doi-asserted-by":"publisher","first-page":"7850","DOI":"10.1002\/mp.15312","volume":"48","author":"J Wu","year":"2021","unstructured":"Wu, J., Hu, R., Xiao, Z., Chen, J., Liu, J.: Vision transformer-based recognition of diabetic retinopathy grade. Med. Phys. 48(12), 7850\u20137863 (2021)","journal-title":"Med. Phys."},{"key":"24_CR29","unstructured":"You, K., Liu, Y., Wang, J., Long, M.: Logme: practical assessment of pre-trained models for transfer learning. In: International Conference on Machine Learning, pp. 12133\u201312143. PMLR (2021)"},{"key":"24_CR30","doi-asserted-by":"crossref","unstructured":"Zamir, A.R., Sax, A., Shen, W., Guibas, L.J., Malik, J., Savarese, S.: Taskonomy: disentangling task transfer learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3712\u20133722 (2018)","DOI":"10.1109\/CVPR.2018.00391"},{"issue":"2","key":"24_CR31","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1109\/JAS.2022.106004","volume":"10","author":"W Zhang","year":"2022","unstructured":"Zhang, W., Deng, L., Zhang, L., Wu, D.: A survey on negative transfer. IEEE\/CAA J. Automatica Sinica 10(2), 305\u2013329 (2022)","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"24_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"issue":"7981","key":"24_CR33","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41586-023-06555-x","volume":"622","author":"Y Zhou","year":"2023","unstructured":"Zhou, Y., et al.: A foundation model for generalizable disease detection from retinal images. Nature 622(7981), 156\u2013163 (2023)","journal-title":"Nature"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-19105-2_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T13:43:57Z","timestamp":1784641437000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-19105-2_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032191045","9783032191052"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-19105-2_24","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}