{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T17:07:02Z","timestamp":1774890422280,"version":"3.50.1"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049834","type":"print"},{"value":"9783032049841","type":"electronic"}],"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\/978-3-032-04984-1_16","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:24:19Z","timestamp":1758299059000},"page":"160-169","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Edge-Semantic Synergy Fusion and\u00a0Adaptive Noise-Aware for\u00a0Weakly Supervised Pathological Tissue Segmentation"],"prefix":"10.1007","author":[{"given":"Hualong","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Siyang","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Zihan","family":"Huan","sequence":"additional","affiliation":[]},{"given":"Huadeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhenbing","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Rushi","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Xipeng","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"issue":"18","key":"16_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":"16_CR2","doi-asserted-by":"crossref","unstructured":"Cai, W., et al.: Enhancing weakly supervised semantic segmentation with multi-label contrastive learning and LLM features guidance. IEEE J. Biomed. Health Inform. (2024)","DOI":"10.1109\/JBHI.2024.3450013"},{"issue":"1","key":"16_CR3","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1038\/s41523-021-00346-1","volume":"7","author":"K El Bairi","year":"2021","unstructured":"El Bairi, K., et al.: The tale of tils in breast cancer: a report from the international immuno-oncology biomarker working group. NPJ Breast Cancer 7(1), 150 (2021)","journal-title":"NPJ Breast Cancer"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Fang, Z., Chen, Y., Wang, Y., Wang, Z., Ji, X., Zhang, Y.: Weakly-supervised semantic segmentation for histopathology images based on dataset synthesis and feature consistency constraint. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 606\u2013613 (2023)","DOI":"10.1609\/aaai.v37i1.25136"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Fang, Z., Wang, Y., Xie, P., Wang, Z., Zhang, Y.: HisynSeg: weakly-supervised histopathological image segmentation via image-mixing synthesis and consistency regularization. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3520129"},{"key":"16_CR6","doi-asserted-by":"publisher","unstructured":"Feng, S., et al.: Mining gold from the sand: weakly supervised histological tissue segmentation with activation relocalization and mutual learning. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15008, pp. 414\u2013423. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72111-3_39","DOI":"10.1007\/978-3-031-72111-3_39"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Han, C., et al.: Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels. Med. Image Anal. 80, 102487 (2022)","DOI":"10.1016\/j.media.2022.102487"},{"key":"16_CR8","doi-asserted-by":"publisher","unstructured":"Li, Y., Yu, Y., Zou, Y., Xiang, T., Li, X.: Online easy example mining for weakly-supervised gland segmentation from histology images. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol.13434, pp. 578\u2013587. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16440-8_55","DOI":"10.1007\/978-3-031-16440-8_55"},{"issue":"12","key":"16_CR9","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1038\/s44222-023-00096-8","volume":"1","author":"AH Song","year":"2023","unstructured":"Song, A.H., et al.: Artificial intelligence for digital and computational pathology. Nat. Rev. Bioeng. 1(12), 930\u2013949 (2023)","journal-title":"Nat. Rev. Bioeng."},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Tang, F., Xu, Z., Qu, Z., Feng, W., Jiang, X., Ge, Z.: Hunting attributes: context prototype-aware learning for weakly supervised semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3324\u20133334 (2024)","DOI":"10.1109\/CVPR52733.2024.00320"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Wang, Y., et\u00a0al.: Computerized tertiary lymphoid structures density on H &E-images is a prognostic biomarker in resectable lung adenocarcinoma. Iscience 26(9) (2023)","DOI":"10.1016\/j.isci.2023.107635"},{"key":"16_CR12","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.patcog.2019.01.006","volume":"90","author":"Z Wu","year":"2019","unstructured":"Wu, Z., Shen, C., Van Den Hengel, A.: Wider or deeper: revisiting the ResNet model for visual recognition. Pattern Recogn. 90, 119\u2013133 (2019)","journal-title":"Pattern Recogn."},{"key":"16_CR13","doi-asserted-by":"publisher","first-page":"105070","DOI":"10.1016\/j.engappai.2022.105070","volume":"114","author":"D Zhang","year":"2022","unstructured":"Zhang, D., Zhao, J., Chen, J., Zhou, Y., Shi, B., Yao, R.: Edge-aware and spectral-spatial information aggregation network for multispectral image semantic segmentation. Eng. Appl. Artif. Intell. 114, 105070 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, H., et\u00a0al.: ResNest: split-attention networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2736\u20132746 (2022)","DOI":"10.1109\/CVPRW56347.2022.00309"},{"key":"16_CR15","doi-asserted-by":"publisher","unstructured":"Zhang, S., Zhang, J., Xie, Y., Xia, Y.: TPRO: text-prompting-based weakly supervised histopathology tissue segmentation. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14220, pp. 109\u2013118. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43907-0_11","DOI":"10.1007\/978-3-031-43907-0_11"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"issue":"10","key":"16_CR17","doi-asserted-by":"publisher","first-page":"2912","DOI":"10.1109\/TMI.2023.3269798","volume":"42","author":"L Zhong","year":"2023","unstructured":"Zhong, L., Wang, G., Liao, X., Zhang, S.: HAMIL: high-resolution activation maps and interleaved learning for weakly supervised segmentation of histopathological images. IEEE Trans. Med. Imaging 42(10), 2912\u20132923 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16_CR18","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\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"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\/978-3-032-04984-1_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:24:26Z","timestamp":1758299066000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04984-1_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049834","9783032049841"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04984-1_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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"}}]}}