Democratizing Advanced High-Throughput Imaging via Cross-Instrument Deep Learning-Enabled Modality Transfer

Abstract

High-throughput imaging is often constrained by a trade-off between acquisition speed and image quality. Fast imaging modalities, such as wide-field fluorescence microscopy, enable large-scale data acquisition but suffer from reduced contrast and resolution, whereas high-resolution techniques, like confocal or super-resolution techniques, provide superior image quality at the cost of reduced throughput and increased instrument time. Here, we present a deep learning-based approach for modality transfer across independent microscopes, enabling the transformation of low-quality images acquired on fast systems into high-quality representations comparable to those obtained using advanced imaging platforms. To achieve this, we employed a generative adversarial network (GAN)-based model trained on paired datasets acquired on physically separate wide-field and confocal microscopes, demonstrating that image quality can be reliably transferred between independent instruments. Importantly, this approach enables a workflow in which high-throughput imaging can be performed on fast, accessible microscopy systems while preserving the ability to computationally recover high-quality structural information. High-resolution microscopy can then be reserved for algorithm training and targeted validation, reducing acquisition time and improving overall experimental efficiency. This workflow supports a model in which shared imaging facilities provide access to advanced instrumentation without requiring individual research groups to procure dedicated high-end systems. Together, our results establish deep learning-enabled modality transfer as a practical strategy for bridging independent microscopy systems and supporting scalable, high-content imaging workflows.