{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:50:07Z","timestamp":1786038607629,"version":"3.56.0"},"reference-count":64,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.compmedimag.2026.102799","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T06:50:31Z","timestamp":1784271031000},"page":"102799","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Learning generalizable representations across Heterogeneous Acquisition Environments for Breast Ultrasound Diagnosis"],"prefix":"10.1016","volume":"134","author":[{"given":"Huanjun","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shukang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentao","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daoqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6094-7250","authenticated-orcid":false,"given":"Peng","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2026.102799_b1","series-title":"Invariant risk minimization","author":"Arjovsky","year":"2019"},{"issue":"4","key":"10.1016\/j.compmedimag.2026.102799_b2","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1111\/rssb.12268","article-title":"Approximate residual balancing: debiased inference of average treatment effects in high dimensions","volume":"80","author":"Athey","year":"2018","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"10.1016\/j.compmedimag.2026.102799_b3","series-title":"Meta-learned invariant risk minimization","author":"Bae","year":"2021"},{"key":"10.1016\/j.compmedimag.2026.102799_b4","series-title":"Qwen-VL: A versatile vision-language model for understanding, localization, text reading, and beyond","author":"Bai","year":"2023"},{"issue":"3","key":"10.1016\/j.compmedimag.2026.102799_b5","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA: Cancer J. Clin."},{"issue":"4","key":"10.1016\/j.compmedimag.2026.102799_b6","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1111\/biom.13825","article-title":"Entropy balancing for causal generalization with target sample summary information","volume":"79","author":"Chen","year":"2023","journal-title":"Biometrics"},{"key":"10.1016\/j.compmedimag.2026.102799_b7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L., et al., 2024. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 24185\u201324198.","DOI":"10.1109\/CVPR52733.2024.02283"},{"key":"10.1016\/j.compmedimag.2026.102799_b8","series-title":"MedMoE: Modality-specialized mixture of experts for medical vision-language understanding","author":"Chopra","year":"2025"},{"issue":"5","key":"10.1016\/j.compmedimag.2026.102799_b9","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1038\/s41591-024-02959-y","article-title":"Vision\u2013language foundation model for echocardiogram interpretation","volume":"30","author":"Christensen","year":"2024","journal-title":"Nature Med."},{"issue":"6","key":"10.1016\/j.compmedimag.2026.102799_b10","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0302527","article-title":"Predictive value of ultrasound doppler parameters in neoadjuvant chemotherapy response of breast cancer: Prospective comparison with magnetic resonance and mammography","volume":"19","author":"Conz","year":"2024","journal-title":"Plos One"},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102799_b11","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1186\/s12891-023-06790-3","article-title":"Influence of ultrasound machine settings on quantitative measures derived from spatial frequency analysis of muscle tissue","volume":"24","author":"Crawford","year":"2023","journal-title":"BMC Musculoskelet. Disord."},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102799_b12","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1038\/s42256-022-00445-z","article-title":"Stable learning establishes some common ground between causal inference and machine learning","volume":"4","author":"Cui","year":"2022","journal-title":"Nat. Mach. Intell."},{"issue":"7","key":"10.1016\/j.compmedimag.2026.102799_b13","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.3390\/cancers15072112","article-title":"Ultrasound for breast cancer screening in resource-limited settings: current practice and future directions","volume":"15","author":"Dan","year":"2023","journal-title":"Cancers"},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102799_b14","doi-asserted-by":"crossref","DOI":"10.2214\/AJR.24.31830","article-title":"Automated breast ultrasound with remote reading for primary breast cancer screening: a prospective study involving 46 community health centers in China","volume":"224","author":"Dang","year":"2025","journal-title":"Am. J. Roentgenol."},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102799_b15","doi-asserted-by":"crossref","DOI":"10.1148\/rg.220093","article-title":"Artifacts and technical considerations at contrast-enhanced US","volume":"43","author":"Fetzer","year":"2022","journal-title":"Radiographics"},{"key":"10.1016\/j.compmedimag.2026.102799_b16","series-title":"2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society","first-page":"480","article-title":"Vision transformers for classification of breast ultrasound images","author":"Gheflati","year":"2022"},{"issue":"4","key":"10.1016\/j.compmedimag.2026.102799_b17","doi-asserted-by":"crossref","first-page":"3110","DOI":"10.1002\/mp.16812","article-title":"BUS-bra: a breast ultrasound dataset for assessing computer-aided diagnosis systems","volume":"51","author":"G\u00f3mez-Flores","year":"2024","journal-title":"Med. Phys."},{"key":"10.1016\/j.compmedimag.2026.102799_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102581","article-title":"Medblip: A multimodal method of medical question-answering based on fine-tuning large language model","volume":"124","author":"Gong","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102799_b19","doi-asserted-by":"crossref","unstructured":"Guo, X., Chai, W., Li, S.-Y., Wang, G., 2024. LLaVA-ultra: Large Chinese language and vision assistant for ultrasound. In: Proceedings of the 32nd ACM International Conference on Multimedia. pp. 8845\u20138854.","DOI":"10.1145\/3664647.3681584"},{"key":"10.1016\/j.compmedimag.2026.102799_b20","doi-asserted-by":"crossref","unstructured":"Guo, J., Shan, X., Wang, G., Chen, D., Lu, R., Tang, S., 2025. LLAUS: A High-Quality Instruction-Tuned Large Vision Language Assistant for UltraSound. In: Proceedings of the 2025 International Conference on Multimedia Retrieval. pp. 398\u2013406.","DOI":"10.1145\/3731715.3733374"},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102799_b21","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1093\/pan\/mpr025","article-title":"Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies","volume":"20","author":"Hainmueller","year":"2012","journal-title":"Political Anal."},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102799_b22","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10994-020-05924-1","article-title":"Conditional variance penalties and domain shift robustness","volume":"110","author":"Heinze-Deml","year":"2021","journal-title":"Mach. Learn."},{"issue":"3","key":"10.1016\/j.compmedimag.2026.102799_b23","doi-asserted-by":"crossref","first-page":"262","DOI":"10.3390\/bioengineering11030262","article-title":"Evaluating the role of breast ultrasound in early detection of breast cancer in low-and middle-income countries: a comprehensive narrative review","volume":"11","author":"Iacob","year":"2024","journal-title":"Bioengineering"},{"key":"10.1016\/j.compmedimag.2026.102799_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102630","article-title":"Inference time correction based on confidence and uncertainty for improved deep-learning model performance and explainability in medical image classification","author":"Jeffrey","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102799_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102619","article-title":"JointDiffusion: Joint representation learning for generative, predictive, and self-explainable AI in healthcare","author":"Kaleta","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102799_b26","doi-asserted-by":"crossref","first-page":"21302","DOI":"10.1038\/s41598-022-23990-4","article-title":"Physical imaging parameter variation drives domain shift","volume":"12","author":"Kilim","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compmedimag.2026.102799_b27","doi-asserted-by":"crossref","unstructured":"Koleilat, T., Asgariandehkordi, H., Rivaz, H., Xiao, Y., 2025. Biomedcoop: Learning to prompt for biomedical vision-language models. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 14766\u201314776.","DOI":"10.1109\/CVPR52734.2025.01376"},{"issue":"4","key":"10.1016\/j.compmedimag.2026.102799_b28","doi-asserted-by":"crossref","first-page":"227","DOI":"10.7863\/jum.1986.5.4.227","article-title":"Artifacts in ultrasound imaging","volume":"5","author":"Kremkau","year":"1986","journal-title":"J. Ultrasound Med."},{"issue":"11","key":"10.1016\/j.compmedimag.2026.102799_b29","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0270392","article-title":"Ultrasound insonation angle and scanning imaging modes for imaging dental implant structures: A benchtop study","volume":"17","author":"Kripfgans","year":"2022","journal-title":"Plos One"},{"key":"10.1016\/j.compmedimag.2026.102799_b30","series-title":"International Conference on Machine Learning","first-page":"5815","article-title":"Out-of-distribution generalization via risk extrapolation (rex)","author":"Krueger","year":"2021"},{"key":"10.1016\/j.compmedimag.2026.102799_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102506","article-title":"Meta-learning guidance for robust medical image synthesis: Addressing the real-world misalignment and corruptions","volume":"121","author":"Lee","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102799_b32","doi-asserted-by":"crossref","first-page":"28541","DOI":"10.52202\/075280-1240","article-title":"Llava-med: Training a large language-and-vision assistant for biomedicine in one day","volume":"36","author":"Li","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2026.102799_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109548","article-title":"Domain generalization via causal fine-grained feature decomposition and learning","volume":"119","author":"Li","year":"2024","journal-title":"Comput. Electr. Eng."},{"issue":"10","key":"10.1016\/j.compmedimag.2026.102799_b34","doi-asserted-by":"crossref","first-page":"4938","DOI":"10.1109\/JBHI.2023.3295078","article-title":"Automatic diagnosis of significant liver fibrosis from ultrasound b-mode images using a handcrafted-feature-assisted deep convolutional neural network","volume":"27","author":"Liu","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.compmedimag.2026.102799_b35","series-title":"Fetalclip: A visual-language foundation model for fetal ultrasound image analysis","author":"Maani","year":"2025"},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102799_b36","doi-asserted-by":"crossref","first-page":"14538","DOI":"10.1038\/s41598-021-93783-8","article-title":"A fuzzy rank-based ensemble of CNN models for classification of cervical cytology","volume":"11","author":"Manna","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compmedimag.2026.102799_b37","series-title":"Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care","first-page":"148","article-title":"Vision mamba for classification of breast ultrasound images","author":"Nasiri-Sarvi","year":"2024"},{"issue":"7","key":"10.1016\/j.compmedimag.2026.102799_b38","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1109\/JBHI.2020.2991043","article-title":"AI in medical imaging informatics: current challenges and future directions","volume":"24","author":"Panayides","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.compmedimag.2026.102799_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102659","article-title":"Med-scot: Structured chain-of-thought reasoning and evaluation for enhancing interpretability in medical visual question answering","author":"Qiao","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102799_b40","series-title":"The risks of invariant risk minimization","author":"Rosenfeld","year":"2020"},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102799_b41","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10462-023-10624-y","article-title":"A survey on training challenges in generative adversarial networks for biomedical image analysis","volume":"57","author":"Saad","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.compmedimag.2026.102799_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.ultras.2023.106940","article-title":"Texture analysis of ultrasound images obtained with different beamforming techniques and dynamic ranges\u2013a robustness study","volume":"131","author":"Seoni","year":"2023","journal-title":"Ultrasonics"},{"key":"10.1016\/j.compmedimag.2026.102799_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102502","article-title":"Weakly supervised multi-modal contrastive learning framework for predicting the HER2 scores in breast cancer","volume":"121","author":"Shi","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102799_b44","doi-asserted-by":"crossref","DOI":"10.3389\/fphy.2024.1398393","article-title":"A survey on deep learning in medical ultrasound imaging","volume":"12","author":"Song","year":"2024","journal-title":"Front. Phys."},{"key":"10.1016\/j.compmedimag.2026.102799_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110078","article-title":"Deep learning for multiple sclerosis lesion classification and stratification using MRI","volume":"192","author":"Umirzakova","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compmedimag.2026.102799_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102670","article-title":"Hallucinated domain generalization network with domain-aware dynamic representation for medical image segmentation","author":"Wang","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102799_b47","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1186\/s12880-025-01885-w","article-title":"Abvlm-q: intelligent quality assessment for abdominal ultrasound standard planes via vision-language modeling","volume":"25","author":"Wang","year":"2025","journal-title":"BMC Med. Imaging"},{"key":"10.1016\/j.compmedimag.2026.102799_b48","series-title":"International Conference on Machine Learning","first-page":"24803","article-title":"A theoretical analysis on independence-driven importance weighting for covariate-shift generalization","author":"Xu","year":"2022"},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102799_b49","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1007\/s00330-023-10057-9","article-title":"Evaluation of standard breast ultrasonography by adding two-dimensional and three-dimensional shear wave elastography: a prospective, multicenter trial","volume":"34","author":"Xu","year":"2024","journal-title":"Eur. Radiol."},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102799_b50","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1109\/TMI.2024.3443119","article-title":"Prompt-driven latent domain generalization for medical image classification","volume":"44","author":"Yan","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2026.102799_b51","article-title":"Automated ultrasound diagnosis via CLIP-GPT synergy: A multimodal framework for image classification and report generation","author":"Yan","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.compmedimag.2026.102799_b52","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"95","article-title":"Diffmic: Dual-guidance diffusion network for medical image classification","author":"Yang","year":"2023"},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102799_b53","article-title":"Clinical applications, challenges, and recommendations for artificial intelligence in musculoskeletal and soft-tissue ultrasound: AJR expert panel narrative review","volume":"222","author":"Yi","year":"2024","journal-title":"Am. J. Roentgenol."},{"issue":"3","key":"10.1016\/j.compmedimag.2026.102799_b54","doi-asserted-by":"crossref","first-page":"730","DOI":"10.2214\/AJR.10.4654","article-title":"Interobserver variability of ultrasound elastography: how it affects the diagnosis of breast lesions","volume":"196","author":"Yoon","year":"2011","journal-title":"Am. J. Roentgenol."},{"key":"10.1016\/j.compmedimag.2026.102799_b55","article-title":"Integrating deep feature extraction and MRI radiomics for survival prediction in breast cancer after neoadjuvant chemotherapy","author":"Yuan","year":"2025","journal-title":"Academic Radiol."},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102799_b56","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s12967-025-07555-3","article-title":"Machine learning-based identification of kbhb-affected tumor cell subsets as prognostic and therapeutic targets in breast cancer","volume":"24","author":"Yuan","year":"2026","journal-title":"J. Transl. Med."},{"key":"10.1016\/j.compmedimag.2026.102799_b57","doi-asserted-by":"crossref","DOI":"10.1016\/j.canlet.2026.218388","article-title":"BPHL promotes TNBC stemness by resolving R-loops via POLR2a lactylation inhibition and BARD1-mediated ubiquitination","author":"Yuan","year":"2026","journal-title":"Cancer Lett."},{"key":"10.1016\/j.compmedimag.2026.102799_b58","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cui, P., Xu, R., Zhou, L., He, Y., Shen, Z., 2021. Deep stable learning for out-of-distribution generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 5372\u20135382.","DOI":"10.1109\/CVPR46437.2021.00533"},{"issue":"7","key":"10.1016\/j.compmedimag.2026.102799_b59","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1109\/TMI.2020.2973595","article-title":"Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation","volume":"39","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2026.102799_b60","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102599","article-title":"Adaptive batch-fusion self-supervised learning for ultrasound image pretraining","volume":"124","author":"Zhang","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102799_b61","series-title":"Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs","author":"Zhang","year":"2023"},{"key":"10.1016\/j.compmedimag.2026.102799_b62","series-title":"Ultraad: Fine-grained ultrasound anomaly classification via few-shot CLIP adaptation","author":"Zhou","year":"2025"},{"key":"10.1016\/j.compmedimag.2026.102799_b63","series-title":"Instruction-following evaluation for large language models","author":"Zhou","year":"2023"},{"key":"10.1016\/j.compmedimag.2026.102799_b64","series-title":"Domain generalization with mixstyle","author":"Zhou","year":"2021"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126001023?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126001023?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T15:27:23Z","timestamp":1786030043000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611126001023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":64,"alternative-id":["S0895611126001023"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102799","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Learning generalizable representations across Heterogeneous Acquisition Environments for Breast Ultrasound Diagnosis","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102799","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102799"}}