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<p>This paper proposes a Prototype-Guided Deformable Memory Transformer (Proto-MemFormer) model for Parkinson’s Disease (PD) MRI classification. In the encoding stage, the model integrates a prototype-guided memory mechanism with a deformable attention structure to dynamically aggregate local morphological features and global semantic information. In the decoding stage, a position-calibrated retrieval module is introduced to enhance cross-sample feature alignment and discriminative representation. Experiments conducted on two public datasets, NTUA-Parkinson and PPMI, demonstrate that the proposed model achieves Accuracy, Precision, Recall, F1-Score, and AUC of 93.45%, 93.72%, 93.21%, 93.46%, and 94.81%, respectively, on the NTUA-Parkinson dataset, outperforming current state-of-the-art deep learning methods. Moreover, in the hyperparameter and training set scaling experiments, the model exhibits performance fluctuations of less than 3%, verifying its stability and robustness under different data and environmental conditions.</p>

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Proto-memformer: deformable memory transformer for Parkinson’s MRI classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月19日

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  • Ziyue Wang,
  • Yisong Yao,
  • Jia Chen

摘要

This paper proposes a Prototype-Guided Deformable Memory Transformer (Proto-MemFormer) model for Parkinson’s Disease (PD) MRI classification. In the encoding stage, the model integrates a prototype-guided memory mechanism with a deformable attention structure to dynamically aggregate local morphological features and global semantic information. In the decoding stage, a position-calibrated retrieval module is introduced to enhance cross-sample feature alignment and discriminative representation. Experiments conducted on two public datasets, NTUA-Parkinson and PPMI, demonstrate that the proposed model achieves Accuracy, Precision, Recall, F1-Score, and AUC of 93.45%, 93.72%, 93.21%, 93.46%, and 94.81%, respectively, on the NTUA-Parkinson dataset, outperforming current state-of-the-art deep learning methods. Moreover, in the hyperparameter and training set scaling experiments, the model exhibits performance fluctuations of less than 3%, verifying its stability and robustness under different data and environmental conditions.