TY - GEN
T1 - Neural Architecture Search for Medical Image Classification via Latent Space and Evolutionary Optimization
AU - Dai, Jiawen
AU - Hung, Kevin
AU - Chui, Kwok Tai
AU - Ho, Raymond
AU - Gu, Jialiang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Deep neural networks have greatly improved medical image classification in a variety of applications and modalities. However, designing these networks manually is often time-consuming and suboptimal. This process can be done automatically by using Neural Architecture Search (NAS), which may offer the potential to identify more efficient and effective models. This paper presents an innovative approach that combines Variational Graph Autoencoders (VGAE) and Evolutionary Algorithms (EA) to optimize the NAS framework, with the goal of efficiently finding deep learning models for medical image classification tasks. Specifically, we leverage the NAS-Bench-101 dataset as the candidate architecture pool and utilize the VGAE model to encode and decode the architecture information of the neural network, thereby simplifying the architecture search process. Additionally, we integrate an evolutionary algorithm to explore the latent space, enhancing the model performance. Our method is validated on several datasets under MedMNIST. Experimental results demonstrate the effectiveness of our framework.
AB - Deep neural networks have greatly improved medical image classification in a variety of applications and modalities. However, designing these networks manually is often time-consuming and suboptimal. This process can be done automatically by using Neural Architecture Search (NAS), which may offer the potential to identify more efficient and effective models. This paper presents an innovative approach that combines Variational Graph Autoencoders (VGAE) and Evolutionary Algorithms (EA) to optimize the NAS framework, with the goal of efficiently finding deep learning models for medical image classification tasks. Specifically, we leverage the NAS-Bench-101 dataset as the candidate architecture pool and utilize the VGAE model to encode and decode the architecture information of the neural network, thereby simplifying the architecture search process. Additionally, we integrate an evolutionary algorithm to explore the latent space, enhancing the model performance. Our method is validated on several datasets under MedMNIST. Experimental results demonstrate the effectiveness of our framework.
KW - Deep Neural Networks
KW - Medical Image Classification
KW - Neural Architecture Search
KW - Variational Graph Autoencoder
UR - https://www.scopus.com/pages/publications/105023143694
U2 - 10.1007/978-981-95-3736-5_15
DO - 10.1007/978-981-95-3736-5_15
M3 - Conference contribution
AN - SCOPUS:105023143694
SN - 9789819537358
T3 - Communications in Computer and Information Science
SP - 207
EP - 219
BT - Neural Computing for Advanced Applications - 6th International Conference, NCAA 2025, Proceedings
A2 - Zhang, Haijun
A2 - Tsang, Kim Fung
A2 - Wang, Fu Lee
A2 - Hung, Kevin
A2 - Hao, Tianyong
A2 - Wang, Zenghui
A2 - Wu, Zhou
A2 - Zhang, Zhao
T2 - 6th International Conference on Neural Computing for Advanced Applications, NCAA 2025
Y2 - 4 July 2025 through 6 July 2025
ER -