{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T05:50:20Z","timestamp":1769233820416,"version":"3.49.0"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031830105","type":"print"},{"value":"9783031830082","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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":54,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Magnetic Resonance Imaging (MRI) can be utilized to study the structure of pediatric brains non-invasively. In practice, low-field MRI scanners are widely adopted for pediatric brain imaging. However, the corresponding acquired MRI data usually suffers from severe artifacts, such as noise and motion. Therefore, an effective Quality Assessment (QA) method is essential. To this end, we design a Multi-Label MambaOut (MLMambaOut) model for the low-field pediatric brain MRI QA challenge. Specifically, we view this challenge as a multi-label classification task, utilizing four stages of gated convolution neural network blocks and ML-Decoder to finish the classification with class balance loss. Furthermore, we explore the performance of Mamba and some advanced models for this challenge. We performed extensive experiments on the challenge data, which is low-field and corrupted with seven kinds of artifacts. The results show that our MLMambaOut achieves superior classification results compared with other methods.<\/jats:p>","DOI":"10.1007\/978-3-031-83008-2_1","type":"book-chapter","created":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T21:06:22Z","timestamp":1740344782000},"page":"3-11","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-Label MambaOut for\u00a0Quality Assessment of\u00a0Low-Field Pediatric Brain MR Images"],"prefix":"10.1007","author":[{"given":"Yueyue","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haotian","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongqing","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"1_CR1","unstructured":"Gu, A., Dao, T.: Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)"},{"key":"1_CR2","unstructured":"Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"14154","DOI":"10.1109\/ACCESS.2023.3243466","volume":"11","author":"S Kastryulin","year":"2023","unstructured":"Kastryulin, S., Zakirov, J., Pezzotti, N., Dylov, D.V.: Image quality assessment for magnetic resonance imaging. IEEE Access 11, 14154\u201314168 (2023)","journal-title":"IEEE Access"},{"key":"1_CR4","unstructured":"Li, X., et al.: Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection. In: Advances in Neural Information Processing Systems, vol. 33, pp. 21002\u201321012 (2020)"},{"issue":"11","key":"1_CR5","doi-asserted-by":"publisher","first-page":"3691","DOI":"10.1109\/TMI.2020.3002708","volume":"39","author":"S Liu","year":"2020","unstructured":"Liu, S., Thung, K.H., Lin, W., Shen, D., Yap, P.T.: Hierarchical nonlocal residual networks for image quality assessment of pediatric diffusion MRI with limited and noisy annotations. IEEE Trans. Med. Imaging 39(11), 3691\u20133702 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. 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Perinatal, Preterm and Paediatric Image Analysis: 8th International Workshop, PIPPI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings, pp. 3\u201316. Springer Nature Switzerland, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-45544-5_1"},{"key":"1_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1007\/978-3-030-59725-2_37","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"E Xu","year":"2020","unstructured":"Xu, E., et al.: Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 386\u2013395. 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