AMIF：具有内置认证的可授权医学图像融合模型
AMIF: Authorizable Medical Image Fusion Model with Built-in Authentication
AI总结：
本文提出AMIF，首个具有内置认证的医学图像融合模型，通过授权访问控制保护知识产权，防止推理泄露。
摘要：
多模态图像融合能够实现精确的病变定位和特征描述，从而提高诊断准确性，增强临床决策并推动其在医学影像研究中的重要性。强大的多模态图像融合模型依赖于高质量、临床代表性的多模态训练数据和精心设计的模型架构。因此，此类专业放射组学模型的开发是标准化采集、临床专业知识和算法设计能力的协作成果，需要保护相关知识产权。然而，当前多模态图像融合模型生成融合输出，但缺乏内置机制来保护知识产权，导致通过推理泄露暴露了专有模型知识和敏感训练数据。例如，恶意用户可以利用融合输出和模型蒸馏或其他基于推理的逆向工程技术来近似专有模型的融合性能。为了解决这个问题，我们提出了AMIF，首个具有内置认证的医学图像融合模型，将授权访问控制整合到图像融合目标中。对于未经授权的使用，AMIF将显式可见的版权标识嵌入融合结果中。相反，通过成功的关键基于认证可以访问高质量的融合结果。
英文摘要：
Multimodal image fusion enables precise lesion localization and characterization for accurate diagnosis, thereby strengthening clinical decision-making and driving its growing prominence in medical imaging research. A powerful multimodal image fusion model relies on high-quality, clinically representative multimodal training data and a rigorously engineered model architecture. Therefore, the development of such professional radiomics models represents a collaborative achievement grounded in standardized acquisition, clinical-specific expertise, and algorithmic design proficiency, which necessitates protection of associated intellectual property rights. However, current multimodal image fusion models generate fused outputs without built-in mechanisms to safeguard intellectual property rights, inadvertently exposing proprietary model knowledge and sensitive training data through inference leakage. For example, malicious users can exploit fusion outputs and model distillation or other inference-based reverse engineering techniques to approximate the fusion performance of proprietary models. To address this issue, we propose AMIF, the first Authorizable Medical Image Fusion model with built-in authentication, which integrates authorization access control into the image fusion objective. For unauthorized usage, AMIF embeds explicit and visible copyright identifiers into fusion results. In contrast, high-quality fusion results are accessible upon successful key-based authentication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
