{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T15:32:30Z","timestamp":1775316750633,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556304","type":"print"},{"value":"9789819556311","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\/978-981-95-5631-1_36","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T07:07:54Z","timestamp":1769497674000},"page":"508-521","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Digital Pathology Image Domain Generalization Based on\u00a0Collaborative Feature Matching and\u00a0Uncertainty Perturbation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1890-9215","authenticated-orcid":false,"given":"Jinlong","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6910-0075","authenticated-orcid":false,"given":"Weilong","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4601-5388","authenticated-orcid":false,"given":"Wentao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4957-7559","authenticated-orcid":false,"given":"Lifeng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Han, T., et al.: Privacy-preserving multi-source domain adaptation for medical data. IEEE J. Biomed. Health Inform. 27(2), 842\u2013853 (2023)","DOI":"10.1109\/JBHI.2022.3175071"},{"key":"36_CR2","unstructured":"Cuiling, L., et\u00a0al.: Generalizing to unseen domains: a survey on domain generalization. IEEE Trans. Autom. Control 35(8), 21 (2023)"},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, B., Jha, D., Demir, U., Bagci, U.: Domain generalization with correlated style uncertainty. In: Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024, pp. 1989\u20131998. Institute of Electrical and Electronics Engineers Inc. (2024)","DOI":"10.1109\/WACV57701.2024.00200"},{"key":"36_CR4","unstructured":"Li, X., et al.: Uncertainty modeling for out-of-distribution generalization. arXiv e-prints (2022)"},{"key":"36_CR5","unstructured":"Hendrycks, M.N., Dan., et al.: Augmix: a simple data processing method to improve robustness and uncertainty. In: International Conference on Learning Representations (2020)"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Li, Y., Gong, M., Tian X., Liu, T., Tao, D: Domain generalization via conditional invariant representation (2018)","DOI":"10.1609\/aaai.v32i1.11682"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, M., Li, R., Jia, K., Zhang, L.: Exact feature distribution matching for arbitrary style transfer and domain generalization. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00787"},{"key":"36_CR8","unstructured":"Zellinger, W., Grubinger, T., Lughofer, E., Natschlger, T., Saminger-Platz, S.: Central Moment Discrepancy (CMD) for domain-invariant representation learning (2017)"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Kalischek, N., Wegner, J.D., Schindler, K.: In the light of feature distributions: moment matching for neural style transfer (2021)","DOI":"10.1109\/CVPR46437.2021.00926"},{"key":"36_CR10","unstructured":"Vedantam, R., Lopez-Paz, D., Schwab. D.J.: An empirical investigation of domain generalization with empirical risk minimizers. Adv. Neural Inf. Process. Syst. 34, 28131\u201328143 (2021)"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Nam, H., Lee, H.J., Park, J., Yoon, W., Yoo, D.: Reducing domain gap by reducing style bias. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 8690\u20138699 (2021)","DOI":"10.1109\/CVPR46437.2021.00858"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, Y., Hospedales, T., Xiang, T.: Deep domain-adversarial image generation for domain generalisation. In: Proceedings of the AAAI Conference on Artificial Intelligence 34(7), 13025\u201313032 (2020)","DOI":"10.1609\/aaai.v34i07.7003"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Yang, H., et al.: Domain invariant masked autoencoders for self-supervised learning from multi-domains. arXiv e-prints (2022)","DOI":"10.1007\/978-3-031-19821-2_9"},{"key":"36_CR14","unstructured":"Shankar, S., et al.: Generalizing across domains via cross-gradient training, Siddhartha Chaudhuri (2018)"},{"key":"36_CR15","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. JMLR.org (2014)"},{"key":"36_CR16","unstructured":"Zheng, K., Cao, Y., Zhu, K., Zhao, R., Zha, Z.J.: A frequency-aware MLP-like architecture for domain generalization, Famlp (2022)"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Wang, J.Y., Ruoyi, D., Chang, D., Liang, K., Ma, Z.: Domain generalization via frequency-domain-based feature disentanglement and interaction. In: Proceedings of the 30th ACM International Conference on Multimedia (2022)","DOI":"10.1145\/3503161.3548267"},{"key":"36_CR18","unstructured":"Scalbert, M., Vakalopoulou, M., Couzini\u00e9-Devy, F.: Towards domain-invariant self-supervised learning with batch styles standardization (2023)"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Guo, J., Wang, N., Qi, L., Shi, Y.: Aloft: a lightweight MLP-like architecture with dynamic low-frequency transform for domain generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 24132\u201324141 (2023)","DOI":"10.1109\/CVPR52729.2023.02311"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. IEEE (2017)","DOI":"10.1109\/ICCV.2017.167"},{"key":"36_CR21","unstructured":"Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Domain generalization with mixstyle (2021)"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Nuriel, O., Benaim, S., Wolf, L.: Reducing the bias towards global statistics in image classification, Permuted Adain (2020)","DOI":"10.1109\/CVPR46437.2021.00936"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Maximizing quality and diversity in feed-forward stylization and texture synthesis. IEEE, Improved texture networks (2017)","DOI":"10.1109\/CVPR.2017.437"},{"key":"36_CR24","unstructured":"Zhang, Y., Deng, B., Li, R., Jia, K., Zhang, L.: Adversarial style augmentation for domain generalization (2023)"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Pratt, William, K.: Digital image processing. J. Electron. Imaging 16(2), 131\u2013145 (2007)","DOI":"10.1117\/1.2744044"},{"key":"36_CR26","unstructured":"Risser, E., Wilmot, P., Barnes, C.: Stable and controllable neural texture synthesis and style transfer using histogram losses (2017)"},{"key":"36_CR27","doi-asserted-by":"crossref","unstructured":"Bandi, P., et al.: From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge. Inst. Electr. Electron. Eng. (2) (2019)","DOI":"10.1109\/TMI.2018.2867350"},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"Beck, A.H., et al.: Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci. Transl. Med. 3(108), 108ra113 (2011)","DOI":"10.1126\/scitranslmed.3002564"},{"key":"36_CR29","doi-asserted-by":"crossref","unstructured":"Nina, L., et al.: Identification of tumor epithelium and stroma in tissue microarrays using texture analysis. Diagn. Pathol. 7(1), 22 (2012)","DOI":"10.1186\/1746-1596-7-22"},{"key":"36_CR30","doi-asserted-by":"crossref","unstructured":"Kather, J.N., et al.: Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Medicine, 16(1) (2019)","DOI":"10.1371\/journal.pmed.1002730"},{"key":"36_CR31","unstructured":"Akbarnejad, A., Ray, N., Barnes, P.J., Bigras, G.: Predicting ki67, er, pr, and her2 statuses from he-stained breast cancer images (2023)"},{"issue":"2","key":"36_CR32","doi-asserted-by":"publisher","first-page":"100398","DOI":"10.1016\/j.modpat.2023.100398","volume":"37","author":"L Danni","year":"2024","unstructured":"Danni, L., Wen, Z., et al.: Deep learning ebased h-score quantification of immunohistochemistry-stained images. Mod. Pathol. 37(2), 100398\u2013100398 (2024)","journal-title":"Mod. Pathol."},{"key":"36_CR33","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Domain adaptive ensemble learning (2020)","DOI":"10.1109\/TIP.2021.3112012"},{"key":"36_CR34","doi-asserted-by":"crossref","unstructured":"Peng, T., et al.: Style factorization: explore diverse style variation for domain generalization. In: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7330\u20137334 (2024)","DOI":"10.1109\/ICASSP48485.2024.10447540"},{"key":"36_CR35","unstructured":"Parascandolo, G., Neitz, A., Orvieto, A., Gresele, L., Schlkopf, B.: Learning explanations that are hard to vary (2020)"},{"key":"36_CR36","unstructured":"Wang, L., Wang, J., Li, H., Chen, Y., Xie, X.: Domain-invariant feature exploration for domain generalization (2022)"},{"key":"36_CR37","unstructured":"Krueger, D., et al.: Out-of-distribution generalization via risk extrapolation (rex) (2021)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5631-1_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T14:43:33Z","timestamp":1775313813000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5631-1_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556304","9789819556311"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5631-1_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"28 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}