In biomedical data analysis, the advancement of deep learning techniques faces challenges stemming from the scarcity of ethically sourced datasets and the prohibitive costs associated with data acquisition. In response to these obstacles, we introduce a novel hybrid training model that operates on a blended dataset comprising both real and synthetic data concurrently. Our approach, HybridMorph, is a pairwise medical image registration model built upon the foundations of VoxelMorph [1]. It leverages two distinct training strategies: an unsupervised setting geared towards optimizing standard image matching and a supervised setting incorporating auxiliary segmentation to enhance accuracy. Additionally, we draw inspiration from SynthMorph [2], a synthetic image-based training model capable of accommodating various image types and intensities. Our research showcases the efficacy of transferring knowledge gleaned from Synthmorph models to fine-tune weights for specific tasks, culminating in successful trials of few-shot learning and rapid adaptation capabilities. These methodologies illustrate how hybrid approaches can streamline training processes, mitigate computational resource demands, and alleviate the substantial data prerequisites inherent in contemporary deep learning models. Furthermore, our exploration into transfer learning underscores the potency of such techniques, paving the way for examining meta-learning, multi-task learning, and other avenues to mitigate data dependencies and complexity. Moreover, we are pleased to offer open access to our code repository at https://github.com/CaffineAddic/HybridMorph.
