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Alternative Title
Name pinyin
YUE Zhixiao
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070105 运筹学与控制论
Subject category of dissertation
07 理学
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Medical images are generally used to represent the interior structure of the human body and play a very essential part in illness diagnosis and therapy. Medical image classification and reconstruction are major research fields in medical image analysis. How to increase the accuracy and efficiency of medical image classification is an ongoing research area. Deep learning-based algorithms have demonstrated significant benefits in image classification in recent years. In general, the availability of an extensive dataset is a necessary condition for such state-of-the-art approaches to perform successfully. However, when it comes to classifying electroencephalogram (EEG) signals, obtaining a large amount of data is impracticable owing to issues of safety, protection, and privacy. Meta-learning algorithms can bring fresh ideas in the challenge of few-shot EEG signal classification without relying on large datasets. The concept of meta-learning is to gather previous knowledge by training on other relevant data, and then fine-tune the training model on actual samples. This is a method that is capable of rapidly adapting to new samples depending on prior experience. Meta-learning can be considered as a bi-level programming problem due to its two-stage optimization process.

In this thesis, we begin by preprocessing the raw EEG data and then classifying it using bi-level programming techniques (i.e., meta-learning algorithms). Furthermore, we propose a new three-level programming model on the basis of the bi-level programming by introducing a feasible set of the lower-level variables. Due to the presence of the third-level constraint, the previous meta-learning approaches become inapplicable. Inspired by the BDA and ADMM algorithms, we present a new algorithm and its convergence analysis. Following that, we evaluate and contrast meta-learning approaches to the more traditional ones. The experimental results demonstrate the meta-learning method's success in training EEG prediction models, which is critical for the development of non-invasive Brain-Machine Interfaces (BMIs).

Reconstruction of medical images is also a hot topic in the medical industry. The essence of the image reconstruction problem is to solve an inverse problem, that is, to rebuild the original signal from the acquired signal or observation data. Because this requires denoising, model building, and other challenges, it is not a simple or straightforward problem to solve. To reconstruct the original signal of medical images, we developed a bi-level programming model inspired by the elastic net and lp-l1-2 model. The top objective function is a mixture of the l1 and l2 norms; the lower objective function is utilized to deal with noise pollution. This bi-level programming problem is difficult to solve due to the non-smoothness of the upper layer objective. As a result, we first leverage the properties of the bottom layer objective function to convert the bi-level programming model to a single-layer problem under certain prior information, and then solve this optimization problem using the alternating direction method of multipliers (ADMM). Numerical results demonstrate that this model and the associated algorithm are both practical and successful for reconstructing medical images that have been affected by various noise sources.

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References List

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