{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:43:55Z","timestamp":1761007435928,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSERC funding","award":["RGPIN-2020-05471"],"award-info":[{"award-number":["RGPIN-2020-05471"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>The limited availability of pixel-level annotated medical images complicates training supervised segmentation models, as these models require large datasets. To deal with this issue, SemiSeg-CAW, a semi-supervised segmentation framework that leverages class-level information and an adaptive multi-loss function, is proposed to reduce dependency on extensive annotations. The model combines segmentation and classification tasks in a multitask architecture that includes segmentation, classification, weight generation, and ClassElevateSeg modules. In this framework, the ClassElevateSeg module is initially pre-trained and then fine-tuned jointly with the main model to produce auxiliary feature maps that support the main model, while the adaptive weighting strategy computes a dynamic combination of classification and segmentation losses using trainable weights. The proposed approach enables effective use of both labeled and unlabeled images with class-level information by compensating for the shortage of pixel-level labels. Experimental evaluation on two public ultrasound datasets demonstrates that SemiSeg-CAW consistently outperforms fully supervised segmentation models when trained with equal or fewer labeled samples. The results suggest that incorporating class-level information with adaptive loss weighting provides an effective strategy for semi-supervised medical image segmentation and can improve the segmentation performance in situations with limited annotations.<\/jats:p>","DOI":"10.3390\/make7040124","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T12:27:29Z","timestamp":1760963249000},"page":"124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SemiSeg-CAW: Semi-Supervised Segmentation of Ultrasound Images by Leveraging Class-Level Information and an Adaptive Multi-Loss Function"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0693-5335","authenticated-orcid":false,"given":"Somayeh","family":"Barzegar","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada"}]},{"given":"Naimul","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, X., Song, L., Liu, S., and Zhang, Y. 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