{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T21:07:26Z","timestamp":1763500046693,"version":"3.44.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049469"},{"type":"electronic","value":"9783032049476"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"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\/978-3-032-04947-6_33","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:32:38Z","timestamp":1758389558000},"page":"344-354","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Medical-Knowledge Driven Multiple Instance Learning for\u00a0Classifying Severe Abdominal Anomalies on\u00a0Prenatal Ultrasound"],"prefix":"10.1007","author":[{"given":"Huanwen","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingxian","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanji","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhao","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ran","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuedong","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guowei","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinru","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Ni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Chen, W., Si, C., et\u00a0al.: Semantic prompt for few-shot image recognition. In: Proceedings of the IEEE\/CVF Conference on CVPR, pp. 23581\u201323591 (2023)","DOI":"10.1109\/CVPR52729.2023.02258"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Ciobanu, S.G., Enache, I.A., Iovoaica-R\u0103mescu, C., Berbecaru, E.I.A., et\u00a0al.: Automatic identification of fetal abdominal planes from ultrasound images based on deep learning. J. Imaging Inform. Med. 1\u20138 (2025)","DOI":"10.1007\/s10278-025-01409-6"},{"issue":"1","key":"33_CR3","first-page":"1","volume":"3","author":"Y Gu","year":"2021","unstructured":"Gu, Y., Tinn, R., Cheng, H., Lucas, M., et al.: Domain-specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthc. (HEALTH) 3(1), 1\u201323 (2021)","journal-title":"ACM Trans. Comput. Healthc. (HEALTH)"},{"key":"33_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/978-3-031-43898-1_22","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023","author":"Y Huang","year":"2023","unstructured":"Huang, Y., et al.: Fourier test-time adaptation with multi-level consistency for robust classification. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14222, pp. 221\u2013231. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43898-1_22"},{"key":"33_CR5","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":"33_CR6","doi-asserted-by":"crossref","unstructured":"Ilse, M., Tomczak, J.M., Welling, M.: Deep multiple instance learning for digital histopathology. In: Handbook of Medical Image Computing and Computer Assisted Intervention, pp. 521\u2013546. Elsevier (2020)","DOI":"10.1016\/B978-0-12-816176-0.00027-2"},{"key":"33_CR7","first-page":"20689","volume":"35","author":"SA Javed","year":"2022","unstructured":"Javed, S.A., Juyal, D., Padigela, H., Taylor-Weiner, A., Yu, L., Prakash, A.: Additive mil: intrinsically interpretable multiple instance learning for pathology. Adv. Neural. Inf. Process. Syst. 35, 20689\u201320702 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR8","unstructured":"Jin, P., Zhu, B., Yuan, L., Yan, S.: MoH: multi-head attention as mixture-of-head attention. arXiv preprint arXiv:2410.11842 (2024)"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Laurichesse\u00a0Delmas, H., et\u00a0al.: Congenital unilateral renal agenesis: prevalence, prenatal diagnosis, associated anomalies. data from two birth-defect registries. Birth Defects Res. 109(15), 1204\u20131211 (2017)","DOI":"10.1002\/bdr2.1065"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Li, B., et\u00a0al.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the IEEE\/CVF conference on CVPR, pp. 14318\u201314328 (2021)","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"33_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/978-3-030-87237-3_20","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"H Li","year":"2021","unstructured":"Li, H., et al.: DT-MIL: deformable transformer for\u00a0multi-instance learning on\u00a0histopathological image. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 206\u2013216. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_20"},{"issue":"3","key":"33_CR12","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1002\/uog.24843","volume":"59","author":"M Lin","year":"2022","unstructured":"Lin, M., et al.: Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations. Ultrasound Obstetr. Gynecol. 59(3), 304\u2013316 (2022)","journal-title":"Ultrasound Obstetr. Gynecol."},{"key":"33_CR13","doi-asserted-by":"crossref","unstructured":"Lin, T., Yu, Z., Hu, H., Xu, Y., Chen, C.W.: Interventional bag multi-instance learning on whole-slide pathological images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19830\u201319839 (2023)","DOI":"10.1109\/CVPR52729.2023.01899"},{"key":"33_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-3-031-73284-3_6","volume-title":"Machine Learning in Medical Imaging","author":"Z Liu","year":"2024","unstructured":"Liu, Z., et al.: Mitral regurgitation recogniton based on unsupervised out-of-distribution detection with residual diffusion amplification. In: Xu, X., Cui, Z., Rekik, I., Ouyang, X., Sun, K. (eds.) MLMI 2024. LNCS, vol. 15241, pp. 52\u201362. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-73284-3_6"},{"key":"33_CR15","doi-asserted-by":"publisher","first-page":"891896","DOI":"10.3389\/fsurg.2022.891896","volume":"9","author":"E Pechriggl","year":"2022","unstructured":"Pechriggl, E., Blumer, M., et al.: Embryology of the abdominal wall and associated malformations\u2013a review. Front. Surg. 9, 891896 (2022)","journal-title":"Front. Surg."},{"key":"33_CR16","unstructured":"Qi, Y., et al.: Multi-center study on deep learning-assisted detection and classification of fetal central nervous system anomalies using ultrasound imaging. arXiv preprint arXiv:2501.02000 (2025)"},{"key":"33_CR17","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"issue":"4","key":"33_CR18","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1016\/j.jpurol.2014.03.004","volume":"10","author":"O Sarhan","year":"2014","unstructured":"Sarhan, O., Alghanbar, M., Alsulaihim, A., et al.: Multicystic dysplastic kidney: impact of imaging modality selection on the initial management and prognosis. J. Pediatr. Urol. 10(4), 645\u2013649 (2014)","journal-title":"J. Pediatr. Urol."},{"key":"33_CR19","first-page":"2136","volume":"34","author":"Z Shao","year":"2021","unstructured":"Shao, Z., et al.: TransMIL: transformer based correlated multiple instance learning for whole slide image classification. Adv. Neural. Inf. Process. Syst. 34, 2136\u20132147 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR20","doi-asserted-by":"crossref","unstructured":"Shiku, K., Nishimura, K., Suehiro, D., Tanaka, K., Bise, R.: Ordinal multiple-instance learning for ulcerative colitis severity estimation with selective aggregated transformer. arXiv preprint arXiv:2411.14750 (2024)","DOI":"10.1109\/WACV61041.2025.00421"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Voita, E., Talbot, D., Moiseev, F., Sennrich, R., Titov, I.: Analyzing multi-head self-attention: specialized heads do the heavy lifting, the rest can be pruned. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5797\u20135808 (2019)","DOI":"10.18653\/v1\/P19-1580"},{"key":"33_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/978-3-030-59725-2_19","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"J Wang","year":"2020","unstructured":"Wang, J., et al.: Auto-weighting for breast cancer classification in multimodal ultrasound. In: Martel, A.L., et al. (eds.) MICCAI 2020, Part VI. LNCS, vol. 12266, pp. 190\u2013199. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59725-2_19"},{"key":"33_CR23","doi-asserted-by":"publisher","first-page":"102673","DOI":"10.1016\/j.media.2022.102673","volume":"83","author":"X Wang","year":"2023","unstructured":"Wang, X., Tang, F., Chen, H., Cheung, C.Y., Heng, P.A.: Deep semi-supervised multiple instance learning with self-correction for DME classification from oct images. Med. Image Anal. 83, 102673 (2023)","journal-title":"Med. Image Anal."},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, Z., Agarwal, D., Sun, J.: MedCLIP: contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.256"},{"issue":"8","key":"33_CR25","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1007\/s11548-020-02182-3","volume":"15","author":"B Xie","year":"2020","unstructured":"Xie, B., et al.: Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks. Int. J. Comput. Assist. Radiol. Surg. 15(8), 1303\u20131312 (2020). https:\/\/doi.org\/10.1007\/s11548-020-02182-3","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"33_CR26","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1186\/s12889-025-21642-6","volume":"25","author":"X Xie","year":"2025","unstructured":"Xie, X., et al.: Global birth prevalence of major congenital anomalies: a systematic review and meta-analysis. BMC Public Health 25, 449 (2025)","journal-title":"BMC Public Health"},{"key":"33_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/978-3-031-72083-3_28","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024","author":"S Yang","year":"2024","unstructured":"Yang, S., et al.: MambaMIL: enhancing long sequence modeling with sequence reordering in computational pathology. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15004, pp. 296\u2013306. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72083-3_28"},{"key":"33_CR28","doi-asserted-by":"crossref","unstructured":"Yao, H., Zhang, R., Xu, C.: Visual-language prompt tuning with knowledge-guided context optimization. In: Proceedings of the IEEE\/CVF Conference on CVPR, pp. 6757\u20136767 (2023)","DOI":"10.1109\/CVPR52729.2023.00653"},{"key":"33_CR29","unstructured":"Zhang, S., Xu, Y., Usuyama, N., et\u00a0al.: BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. arXiv preprint arXiv:2303.00915 (2023)"},{"key":"33_CR30","unstructured":"Zhang, Y., Zhang, X., Wang, J., Yang, Y., Peng, T., Tong, C.: Mamba2MIL: state space duality based multiple instance learning for computational pathology. arXiv preprint arXiv:2408.15032 (2024)"},{"key":"33_CR31","unstructured":"Zhuang, L., Ivezic, V., et\u00a0al.: Patient-level thyroid cancer classification using attention multiple instance learning on fused multi-scale ultrasound image features. In: AMIA Annual Symposium Proceedings, vol.\u00a02023, p.\u00a01344 (2024)"}],"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\/978-3-032-04947-6_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:32:45Z","timestamp":1758389565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04947-6_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032049469","9783032049476"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04947-6_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 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"}}]}}