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

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