Deep Learning OCT Conversion to Virtual Stained Skin Images

The patent uses deep learning models to generate synthetic stained images from OCT images, creating virtual copies of stained tissue sections without actual staining. The generative model learns the mapping relationship between OCT and stained image domains, producing realistic stained image representations that preserve diagnostic information while eliminating the time-consuming and destructive staining process.

The patent replaces the mechanical/chemical staining process with a computational deep learning system. Instead of physically staining tissue sections through complex laboratory procedures, the system uses neural networks to transform OCT images into virtual stained images, substituting a mechanical/chemical process with an information processing approach.

The system generates virtual stained images that replicate the appearance and diagnostic characteristics of traditional stained tissue sections. By creating synthetic images in the familiar stained image domain rather than requiring dermatologists to interpret OCT images directly, the system preserves high-resolution imaging capabilities while restoring diagnostic comfort through visual familiarity.

The deep learning model acts as an intermediary that translates OCT images into virtual stained images. This intermediary transformation bridges the gap between the advanced OCT imaging modality and the dermatologists' existing expertise in stained image interpretation, allowing them to maintain their diagnostic workflow while benefiting from non-invasive high-resolution imaging.