人工智能研究者 / 技术负责人
计算机科学与人工智能博士（诺丁汉大学），主要研究方向为医学人工智能，专注于医学影像分析与深度学习方法在疾病诊断中的应用。研究领域涵盖阿尔茨海默症智能诊断、跨模态医学影像生成以及可解释人工智能在外科手术技能评估中的应用。
中国 温州
主导开发主干网络为 U-Net 的 Diffusion 模型进行跨模态医学影像生成，同时设计单向与双向生成范式，通过常规 MRI 合成高精度 PET 影像。其中双向生成范式大搞 SSIM 0.9380 和 PSNR 26.47。MRI 和生成的 PET 联合诊断准确率提升 1.96%。研究成果发表于中科院一区 TOP 期刊 Alzheimer’s & Dementia。负责项目的全部流程，包括算法设计、数据处理、模型训练及论文撰写等。
主导开发主干网络为 U-Net 的 GAN 模型进行跨模态医学影像生成，同时构建多尺度损失函数，通过常规 MRI 合成高精度 PET 影像（SSIM 0.9714/PSNR 31.53），解决阿尔兹海默症诊断中 PET 检查成本高的痛点。MRI 和生成的 PET 联合诊断准确率达 90.18%（Δ+0.87%）。研究成果发表于中科院一区 TOP 期刊 Neural Networks。负责项目的全部流程，包括算法设计、数据处理、模型训练及论文撰写等。
主导开发基于整流视频输入的外科手术技能评级分类模型。在 JIGASAWS 数据集上达到了 SOTA 的准确率（Suturing 100% / Needle Passing 96.3%/ Knot Tying 97.2%）。同时提供了可解释性方案，包括基于 CAM 的和机遇 Snippet 的时序可解释性可视化。部分成果拿到了一项发明专利（已获授权）。负责项目的全部流程，包括算法设计、数据处理、模型训练及论文撰写等。
Ke Chen, Ying Weng, Yueqin Huang, Yiming Zhang, Tom Dening, Akram A. Hosseini, Weizhong Xiao
INTRODUCTION This study presents a novel multi-view learning approach for machine learning (ML)–based Alzheimer's disease (AD) diagnosis. METHODS A diffusion model is proposed to synthesize the fluorodeoxyglucose positron emission tomography (FDG PET) view from the magnetic resonance imaging T1 weighted imaging (MRI T1WI) view and incorporate two synthesis strategies: one-way synthesis and two-way synthesis. To assess the utility of the synthesized views, we use multilayer perceptron (MLP)–based classifiers with various combinations of the views. RESULTS The two-way synthesis achieves state-of-the-art performance with a structural similarity index measure (SSIM) at 0.9380 and a peak-signal-to-noise ratio (PSNR) at 26.47. The one-way synthesis achieves an SSIM at 0.9282 and a PSNR at 23.83. Both synthesized FDG PET views have shown their effectiveness in improving diagnostic accuracy. DISCUSSION This work supports the notion that ML-based cross-domain data synthesis can be a useful approach to improve AD diagnosis by providing additional synthesized disease-related views for multi-view learning.
Ke CHEN, Ying Weng, Akram A. Hosseini, Tom Dening, Guokun Zuo, Yiming Zhang
Alzheimer’s Disease (AD) is a neurodegenerative disease that commonly occurs in older people. It is characterized by both cognitive and functional impairment. However, as AD has an unclear pathological cause, it can be hard to diagnose with confidence. This is even more so in the early stage of Mild Cognitive Impairment (MCI). This paper proposes a U-Net based Generative Adversarial Network (GAN) to synthesize fluorodeoxyglucose - positron emission tomography (FDG-PET) from magnetic resonance imaging - T1 weighted imaging (MRI-T1WI) for further usage in AD diagnosis including its early-stage MCI. The experiments have displayed promising results with Structural Similarity Index Measure (SSIM) reaching 0.9714. Furthermore, three types of classifiers are developed, i.e., one Multi-Layer Perceptron (MLP) based classifier, two Graph Neural Network (GNN) based classifiers where one is for graph classification and the other is for node classification. 10-fold cross-validation has been conducted on all trials of experiments for classifier comparison. The performance of these three types of classifiers has been compared with the different input modalities setting and data fusion strategies. The results have shown that GNN based node classifier surpasses the other two types of classifiers, and has achieved the state-of-the-art (SOTA) performance with the best accuracy at 90.18% for 3-class classification, namely AD, MCI and normal control (NC) with the synthesized fluorodeoxyglucose - positron emission tomography (FDG-PET) features fused at the input level. Moreover, involving synthesized FDG-PET as part of the input with proper data fusion strategies has also proved to enhance all three types of classifiers’ performance. This work provides support for the notion that machine learning-derived image analysis may be a useful approach to improving the diagnosis of AD.
某学会
表彰在智能体系统方向的研究与工程贡献。
