MSP-SR: Multi-Stage Probabilistic Generative Super Resolution with Scarce High-Resolution Data

Ruike Zhu, Matthew Charles Weston, Hanwen Zhang, Arindam Banerjee

Keywords: Diffusion Models, Few-shot Learning, Transfer Learning, Medical Image Super-resolution, Uncertainty Quantification

TL;DR: We present a multi-stage pre-training framework for medical image super-resolution that addresses the challenge of limited high-resolution training data.

Abstract:

Several application domains, especially in science and medicine, benefit tremendously from acquiring high-resolution images of objects and phenomena of interest. Recognizing this need, generative models for super-resolution (SR) have emerged as a promising approach for such data generation. However, when training data are scarce due to high acquisition costs, such models struggle and often fail to capture the true data distribution due to insufficient data and domain knowledge. While transfer learning, domain adaptation, or few shot learning of such generative models can be a reasonable approach, most existing large scale generative models have been (pre)trained on natural images and it is unclear if such models can be seamlessly transferred to say medical images.

In this paper, we propose Multi-Stage Probabilistic Super Resolution (MSP-SR), a cascaded few-shot learning framework for super-resolution through multi-stage transfer learning. At a high level, MSP-SR first transfers a generative model from out-of-domain to in-domain, e.g., from natural to medical images, and then from in-domain to the target application. We present the details based on conditional diffusion models and validate MSP-SR on multiple Magnetic Resonance Imaging (MRI) datasets, demonstrating that MSP-SR persistently and usually significantly outperforms direct fine-tuning (DFT) approaches as well as SR baselines. Further, MSP-SR empirically provides more accurate characterization of uncertainty in SR compared to DFT.

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Submission Number: 477