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Luonan Chen

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NeurIPS Conference 2025 Conference Paper

Self-Assembling Graph Perceptrons

  • Jialong Chen
  • Tong Wang
  • Bowen Deng
  • Luonan Chen
  • Zibin Zheng
  • Chuan Chen

Inspired by the workings of biological brains, humans have designed artificial neural networks (ANNs), sparking profound advancements across various fields. However, the biological brain possesses high plasticity, enabling it to develop simple, efficient, and powerful structures to cope with complex external environments. In contrast, the superior performance of ANNs often relies on meticulously crafted architectures, which can make them vulnerable when handling complex inputs. Moreover, overparameterization often characterizes the most advanced ANNs. This paper explores the path toward building streamlined and plastic ANNs. Firstly, we introduce the Graph Perceptron (GP), which extends the most fundamental ANN, the Multi-Layer Perceptron (MLP). Subsequently, we incorporate a self-assembly mechanism on top of GP called Self-Assembling Graph Perceptron (SAGP). During training, SAGP can autonomously adjust the network's number of neurons and synapses and their connectivity. SAGP achieves comparable or even superior performance with only about 5% of the size of an MLP. We also demonstrate the SAGP's advantages in enhancing model interpretability and feature selection.

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AIIM Journal 2020 Journal Article

Identification of Alzheimer's disease based on wavelet transformation energy feature of the structural MRI image and NN classifier

  • Jinwang Feng
  • Shao-Wu Zhang
  • Luonan Chen

Alzheimer's disease (AD) is now difficult to be identified for clinicians, especially, at its prodromal stage, mild cognitive impairment (MCI), because of no obvious clinical symptom and few impacts on daily life at this phase. In addition, energy distribution differences of brain atrophies reflected in structural magnetic resonance imaging (sMRI) images between MCI patients and older healthy controls (HC) are minimal and subtle, which are difficult to be captured by the spatial analysis. In this study, we propose a novel method (namely AD-WTEF) to identify AD and MCI patients from HC subjects by extracting the wavelet transformation energy feature (WTEF) of the sMRI image. AD-WTEF firstly transforms each scan of the preprocessed sMRI image by wavelet to obtain its directional subbands with the same size at different transformation levels. And then, based on the anatomical automatic labeling (AAL) atlas, AD-WTEF constructs a new brain mask to segment the subbands at the same direction and transformation level into different energy regions of interest (EROIs). Thirdly, by averaging coefficients in an EROI, AD-WTEF gets an energy feature, following that energy features of different EROIs are connected to form an energy feature vector for describing the subbands at the same direction and transformation level. As a result, these energy feature vectors are further concatenated to be a WTEF of the sMRI image. Finally, the nearest neighbor (NN) classifier is selected and used for AD identification. Compared with other seven state-of-the-art methods, our AD-WTEF can effectively identify AD patients using the subtle energy distribution differences of sMRI images. Furthermore, experimental results indicate that our AD-WTEF can also find important brain ROIs related to AD.

Details DOI
v2026.09.13