One Hyper-Initializer for All Network Architectures in Medical Image Analysis

Fangxin Shang; Yehui Yang; Dalu Yang; Junde Wu; Xiaorong Wang; Yanwu Xu

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arXiv · 2206.03661

One Hyper-Initializer for All Network Architectures in Medical Image Analysis

Fangxin Shang·Yehui Yang·Dalu Yang·Junde Wu·Xiaorong Wang·Yanwu Xu

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Abstract

Pre-training is essential to deep learning model performance, especially in medical image analysis tasks where limited training data are available. However, existing pre-training methods are inflexible as the pre-trained weights of one model cannot be reused by other network architectures. In this paper, we propose an architecture-irrelevant hyper-initializer, which can initialize any given network architecture well after being pre-trained for only once. The proposed initializer is a hypernetwork which takes a downstream architecture as input graphs and outputs the initialization parameters of the respective architecture. We show the effectiveness and efficiency of the hyper-initializer through extensive experimental results on multiple medical imaging modalities, especially in data-limited fields. Moreover, we prove that the proposed algorithm can be reused as a favorable plug-and-play initializer for any downstream architecture and task (both classification and segmentation) of the same modality.

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Fangxin Shang, Yehui Yang, Dalu Yang, Junde Wu, Xiaorong Wang, Yanwu Xu. 2022-06-08. One Hyper-Initializer for All Network Architectures in Medical Image Analysis. https://arxiv.org/abs/2206.03661

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