用轻量区块链追踪医学影像修复过程，确保结果可信可查。
Provenance-Driven Reliable Semantic Medical Image Vector Reconstruction via Lightweight Blockchain-Verified Latent Fingerprints
- 用语义嵌入+混合U-Net重建医学影像，保留关键解剖结构。
- 在多个数据集上验证，结构一致性与恢复精度均优于现有方法。
- 区块链记录修复痕迹，适合对可追溯性要求高的医疗场景。
医学影像对临床诊断至关重要，但真实数据常受噪声、损坏或篡改影响，威胁AI辅助解读的可靠性。传统重建方法侧重像素级恢复，可能生成视觉逼真但解剖失真的结果，直接影响临床判断。本文提出一种语义感知的医学图像重建框架，结合高层隐空间嵌入与混合U-Net结构，在修复过程中保持临床相关结构的完整性。为保障信任与责任可追溯性，引入基于无标度图设计的轻量级区块链溯源层，实现修复事件的可验证记录，且开销极小。在多个数据集和多种污染类型下的广泛评估表明，该方法在结构一致性、恢复准确性和溯源完整性方面均优于现有技术。通过融合语义引导重建与安全可追溯性，本方案提升了医疗影像AI的可靠性，增强了诊断信心与医疗监管合规性。
原文摘要 · Abstract (English)
Medical imaging is essential for clinical diagnosis, yet real-world data frequently suffers from corruption, noise, and potential tampering, challenging the reliability of AI-assisted interpretation. Conventional reconstruction techniques prioritize pixel-level recovery and may produce visually plausible outputs while compromising anatomical fidelity, an issue that can directly impact clinical outcomes. We propose a semantic-aware medical image reconstruction framework that integrates high-level latent embeddings with a hybrid U-Net architecture to preserve clinically relevant structures during restoration. To ensure trust and accountability, we incorporate a lightweight blockchain-based provenance layer using scale-free graph design, enabling verifiable recording of each reconstruction event without imposing significant overhead. Extensive evaluation across multiple datasets and corruption types demonstrates improved structural consistency, restoration accuracy, and provenance integrity compared with existing approaches. By uniting semantic-guided reconstruction with secure traceability, our solution advances dependable AI for medical imaging, enhancing both diagnostic confidence and regulatory compliance in healthcare environments.
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