Selected work

Representative Papers

AI-Driven Radiology Report Generation for Traumatic Brain Injuries.

Jan 30, 2025Journal of imaging informatics in medicine

To address diagnostic delays caused by delayed interpretation of cranial trauma imaging in emergency settings, this study proposes an end-to-end AI system integrating AC-BiFPN and Transformer architectures for multi-scale feature extraction from CT/MRI scans and automatic generation of natural-language radiology reports. The framework uniquely co-optimizes lesion detection accuracy and report semantic coherence in the cranial trauma domain: AC-BiFPN enhances multi-scale lesion localization, while the Transformer captures long-range semantic dependencies to produce structured, clinically interpretable reports. Evaluated on the RSNA Intracranial Hemorrhage dataset, the model achieves significantly higher diagnostic accuracy and report quality compared to conventional CNN-based approaches. This work advances emergency department efficiency and provides an interpretable, deployable solution for clinical decision support and medical education.

3 citationsRead paper

Trustless Accountable Data Sharing for Supply Chains: A Reference Architecture and MoreMedDiet Proof-of-Concept

Aug 31, 2026

本文提出一种基于许可分布式账本和物联网技术的架构，解决供应链中不信任方之间的数据共享问题，同时保护隐私并确保透明度。

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Phase Boundary of a Stochastic Watts-Threshold SIS Model on Random Networks

Jun 29, 2026

This study characterizes the extinction–persistence phase transition boundary of complex contagion models with recovery mechanisms on random networks. Focusing on the Watts threshold SIS model, the authors perform over 180,000 Monte Carlo simulations on Erdős–Rényi and Barabási–Albert networks, combining adaptive Delaunay sampling with weighted logistic regression to quantitatively reconstruct— for the first time—the phase boundary in the joint parameter space of transmission rate, adoption threshold, and infectious duration. The results reveal an exceptionally sharp transition, with the 10%–90% extinction probability bandwidth spanning only 0.005–0.008, and a phase boundary structure invariant across network topologies. The adoption threshold dominates the transition, while transmission rate and infectious duration play secondary, asymmetric roles. This work establishes a benchmark analogous to the classical SIS epidemic threshold for complex contagion and develops a high-precision six-parameter interaction model.

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Regulating the Machine Contributor: Governance and Policy Alignment in Open Source

Jun 12, 2026

This study addresses the challenges posed by autonomous or semi-autonomous AI contributors to human-centric open-source governance mechanisms, which have led to policy fragmentation and misalignment with emerging AI regulations. Employing a most-similar systems design, the research combines policy text analysis, indicator coding, and process tracing to comparatively examine AI contribution policies across six open-source organizations. It proposes the first six-dimensional governance taxonomy and a policy maturity scoring framework specifically tailored for open-source AI contributions. The analysis identifies critical governance failures in dimensions such as disclosure, accountability, and oversight, and reveals significant coordination gaps between existing policies and international AI regulatory standards. Building on these findings, the study outlines an initial, calibratable, tiered coordination governance framework to bridge these disconnects.

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Rabies diagnosis in low-data settings: A comparative study on the impact of data augmentation and transfer learning

Apr 20, 2026

This study addresses the challenges of rabies diagnosis in low-resource regions such as Africa and Asia, where reliance on fluorescence microscopy and expert interpretation is hindered by scarce samples and a shortage of trained personnel. The authors propose a deep learning–based automated diagnostic approach and systematically evaluate multiple transfer learning architectures—including EfficientNetB0, EfficientNetB2, VGG16, and Vision Transformer (ViT)—alongside various data augmentation strategies on a small dataset of only 155 fluorescence images. They demonstrate for the first time that TrivialAugmentWide effectively preserves critical fluorescent features while enhancing model generalization. Among the tested configurations, EfficientNetB0 combined with tailored augmentation achieves optimal performance on cropped images. The results indicate that reliable and rapid automated diagnosis is attainable even under extreme data scarcity and class imbalance, and the method has already been deployed as an online tool for real-world use.

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Recent publications

Latest Papers

Trustless Accountable Data Sharing for Supply Chains: A Reference Architecture and MoreMedDiet Proof-of-Concept

Aug 31, 2026

本文提出一种基于许可分布式账本和物联网技术的架构，解决供应链中不信任方之间的数据共享问题，同时保护隐私并确保透明度。

0 citationsRead paper

Phase Boundary of a Stochastic Watts-Threshold SIS Model on Random Networks

Jun 29, 2026

This study characterizes the extinction–persistence phase transition boundary of complex contagion models with recovery mechanisms on random networks. Focusing on the Watts threshold SIS model, the authors perform over 180,000 Monte Carlo simulations on Erdős–Rényi and Barabási–Albert networks, combining adaptive Delaunay sampling with weighted logistic regression to quantitatively reconstruct— for the first time—the phase boundary in the joint parameter space of transmission rate, adoption threshold, and infectious duration. The results reveal an exceptionally sharp transition, with the 10%–90% extinction probability bandwidth spanning only 0.005–0.008, and a phase boundary structure invariant across network topologies. The adoption threshold dominates the transition, while transmission rate and infectious duration play secondary, asymmetric roles. This work establishes a benchmark analogous to the classical SIS epidemic threshold for complex contagion and develops a high-precision six-parameter interaction model.

0 citationsRead paper

Regulating the Machine Contributor: Governance and Policy Alignment in Open Source

Jun 12, 2026

This study addresses the challenges posed by autonomous or semi-autonomous AI contributors to human-centric open-source governance mechanisms, which have led to policy fragmentation and misalignment with emerging AI regulations. Employing a most-similar systems design, the research combines policy text analysis, indicator coding, and process tracing to comparatively examine AI contribution policies across six open-source organizations. It proposes the first six-dimensional governance taxonomy and a policy maturity scoring framework specifically tailored for open-source AI contributions. The analysis identifies critical governance failures in dimensions such as disclosure, accountability, and oversight, and reveals significant coordination gaps between existing policies and international AI regulatory standards. Building on these findings, the study outlines an initial, calibratable, tiered coordination governance framework to bridge these disconnects.

0 citationsRead paper

Rabies diagnosis in low-data settings: A comparative study on the impact of data augmentation and transfer learning

Apr 20, 2026

This study addresses the challenges of rabies diagnosis in low-resource regions such as Africa and Asia, where reliance on fluorescence microscopy and expert interpretation is hindered by scarce samples and a shortage of trained personnel. The authors propose a deep learning–based automated diagnostic approach and systematically evaluate multiple transfer learning architectures—including EfficientNetB0, EfficientNetB2, VGG16, and Vision Transformer (ViT)—alongside various data augmentation strategies on a small dataset of only 155 fluorescence images. They demonstrate for the first time that TrivialAugmentWide effectively preserves critical fluorescent features while enhancing model generalization. Among the tested configurations, EfficientNetB0 combined with tailored augmentation achieves optimal performance on cropped images. The results indicate that reliable and rapid automated diagnosis is attainable even under extreme data scarcity and class imbalance, and the method has already been deployed as an online tool for real-world use.

0 citationsRead paper

Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks

Mar 26, 2026

This study addresses the challenges of high inter-patient variability and complex temporal dynamics in electroencephalogram (EEG) signals for seizure prediction by proposing an adaptive Transformer framework tailored to individual patients. The approach employs a two-stage training strategy: first, a general-purpose EEG representation is learned via self-supervised pretraining; subsequently, patient-specific fine-tuning is performed by integrating noise-aware preprocessing, multi-channel signal tokenization, and autoregressive modeling. Evaluated on the TUH EEG dataset, the method achieves over 90% accuracy in predicting seizures within a 30-second horizon and attains an F1 score above 0.80, significantly outperforming existing approaches and demonstrating enhanced performance in personalized seizure forecasting.

0 citationsRead paper