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医学分析的进步长期以来一直依赖于尖端技术和临床专业知识的交集。随着基础模型和创新深度学习体系结构的出现，该领域在解决诊断成像，治疗计划和个性化医学的挑战方面取得了显着进步。这些模型，例如SAM（段的任何模型）和Mamba等新兴体系结构等基础框架，提供了前所未有的功能来了解复杂的模式并从多模式医疗数据集中获得可行的见解。本期特刊旨在探索医学分析中基础模型和新型深度学习体系结构的变革潜力。对理论创新，实际实施或经验评估进行深入研究，尤其是那些提高临床适用性并应对诸如注释稀缺，数据可变性和多模式整合等挑战的贡献。提交有望展示优先考虑鲁棒性，可伸缩性和解释性的方法，以确保在各种医疗环境中广泛采用。Topics of interest include, but are not limited to, the following: • Development of foundational deep learning architectures tailored for medical analysis in 2D, 3D, or video data • Applications of foundational and large-scale models (e.g., SAM, Mamba) to enhance performance in multi-modal medical imaging • Advanced multi-modal techniques integrating diverse medical data (MRI, CT, X-ray, ultrasound) for comprehensive analysis • Innovative approaches to weakly supervised and semi-supervised learning for addressing annotation limitations • Cross-domain transfer learning and domain adaptation strategies to enable the adoption of models across different imaging conditions • Integration of attention mechanisms and novel processing techniques to enhance the accuracy and efficiency of medical models • Computational efficiency solutions for deploying foundational models in resource-constrained environments • Real-world clinical validation and applications of deep learning models in healthcare environments • Model interpretability and explainability techniques to make medical models more accessible to clinicians, including visualization and feature attribution tools • Development of benchmark datasets and metrics for evaluating foundational models in medical contexts Guest Editors Shaohua Wan, University of Electronic Science and Technology of China, shaohua.wan@uestc.edu.cn Stefano Berretti, University of Firenze, stefano.berretti@unifi.it Manoranjan Paul，Charles Sturt University，mpaul@csu.edu.edu.edu.au Michele Nappi，Salerno，salerno，Mnappi@unisa.it关键日期日期日期截止日期截止日期的截止日期：提交时间：2025年8月31日，第2025年8月31日，201025年10月31日，2025年10月31日，截止时间为202年3月31日，202年3月31日，202年5月31日；
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