{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T17:06:09Z","timestamp":1788455169691,"version":"build-2803163510"},"reference-count":60,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>The segmentation and classification of breast ultrasound (BUS) images are crucial for the early diagnosis of breast cancer and remain a key focus in BUS image processing. Numerous machine learning and deep learning algorithms have shown their effectiveness in the segmentation and diagnosis of BUS images. In this work, we propose a multi-task learning network with an object contextual attention module (MTL-OCA) for the segmentation and classification of BUS images. The proposed method utilizes the object contextual attention module to capture pixel-region relationships, enhancing the quality of segmentation masks. For classification, the model leverages high-level features extracted from unenhanced segmentation masks to improve accuracy. Cross-validation on a public BUS dataset demonstrates that MTL-OCA outperforms several current state-of-the-art methods, achieving superior results in both classification and segmentation tasks.<\/jats:p>","DOI":"10.3389\/fonc.2025.1567577","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T06:01:33Z","timestamp":1744092093000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Automatic joint segmentation and classification of breast ultrasound images via multi-task learning with object contextual 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