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Generative Adversarial Networks Based on Fine-Grained Image Recognition for the Progression Prediction of Progressive
Changsong Shen1,2, Fangxiang Wu1,3, Bo Liao4,5
1School of Mathematics and Statistics, Hainan Normal University, Haikou, 571158, China.
Abstract:
Progressive mild cognitive impairment (pMCI) often develops into Alzheimer's disease (AD), whereas stable mild cognitive impairment (sMCI) remains cognitively unchanged. Therefore, early identification of pMCI based on multimodal neuroimaging data (e.g., MRI, PET) is clinically valuable. However, limited multimodal data reduces complementary information across modalities and degrades prediction performance. Existing generative adversarial networks (GANs) often overlook local information when synthesizing cross-modal neuroimages, leading to suboptimal image quality. Motivated by these shortcomings, we propose a generative adversarial network (FGGAN) based on fine-grained image recognition for cross-modal image synthesis and pMCI progression prediction. FGGAN comprises a GAN, a feature depth extraction (FDE) module, and a classifier module. The GAN synthesizes high-quality missing modality data by leveraging local and global cues from the input image, while extracting multimodal feature representations. The FDE refines semantic features to improve feature adaptation for the classifier, which predicts pMCI progression from fused multimodal features. Results from the ADNI dataset indicate that FGGAN achieves superior performance in image synthesis quality and disease classification.
Insights
This study introduces FGGAN, a novel method for synthesizing neuroimaging data to improve the early identification of progressive mild cognitive impairment (pMCI), a precursor to Alzheimer's disease (AD). FGGAN enhances prediction accuracy by generating high-quality, multimodal brain images.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Neurology
Background:
- Progressive mild cognitive impairment (pMCI) can advance to Alzheimer's disease (AD), making early identification crucial for timely intervention.
- Current methods using multimodal neuroimaging data (MRI, PET) for pMCI prediction are limited by data scarcity and suboptimal cross-modal synthesis.
- Existing generative adversarial networks (GANs) often fail to capture local details in synthesized neuroimages, impacting prediction performance.
Purpose of the Study:
- To develop a novel generative adversarial network (FGGAN) for high-quality cross-modal neuroimage synthesis.
- To enhance the prediction accuracy of progressive mild cognitive impairment (pMCI) using fused multimodal neuroimaging features.
- To address the limitations of existing GANs in capturing local information for improved image synthesis.
Main Methods:
- Proposed a fine-grained generative adversarial network (FGGAN) incorporating a GAN, feature depth extraction (FDE) module, and a classifier.
- The GAN synthesizes missing neuroimaging modalities using local and global cues, extracting multimodal feature representations.
- The FDE module refines semantic features for improved classifier adaptation, enabling pMCI progression prediction from fused multimodal data.
Main Results:
- FGGAN demonstrated superior performance in synthesizing high-quality cross-modal neuroimages compared to existing methods.
- The proposed method achieved enhanced accuracy in classifying progressive mild cognitive impairment (pMCI) progression.
- Validation on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset confirmed the effectiveness of FGGAN.
Conclusions:
- FGGAN effectively synthesizes multimodal neuroimaging data, overcoming limitations of previous approaches.
- The enhanced image synthesis quality directly contributes to improved prediction of pMCI progression.
- This approach holds significant clinical value for the early identification of individuals at risk of developing Alzheimer's disease.
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