Provenance-Driven Reliable Semantic Medical Image Vector Reconstruction via Lightweight Blockchain-Verified Latent Fingerprints

TL;DR

This paper introduces a semantic-aware medical image reconstruction method that combines high-level latent embeddings with a lightweight blockchain for verifiable provenance, improving structural fidelity and trustworthiness in AI-assisted diagnostics.

Contribution

It presents a novel framework integrating semantic-guided reconstruction with blockchain-based provenance to enhance reliability and accountability in medical imaging AI.

Findings

Improved structural consistency across various datasets.

Enhanced restoration accuracy compared to existing methods.

Verifiable provenance recording with minimal overhead.

Abstract

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.