Unsupervised content-preserving transformation for optical microscopy

The development of deep learning and the open access to a substantial collection of imaging data provide a potential solution to computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner and the reliance on a laborious and error-prone data annotation procedure remains a barrier towards more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases where supervised models cannot apply. By introducing a saliency constraint, the unsupervised model, dubbed as Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data and avoid the distortion of the image content. UTOM shows promising performances in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations elucidate that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.