The traditional histochemical staining of autopsy tissue samples usually suffers from staining artifacts due to autolysis caused by delayed fixation of cadaver tissues. Here, we introduce an autopsy virtual staining technique to digitally convert autofluorescence images of unlabeled autopsy tissue sections into their hematoxylin and eosin (H&E) stained counterparts through a trained neural network. This technique was demonstrated to effectively mitigate autolysis-induced artifacts inherent in histochemical staining, such as weak nuclear contrast and color fading in the cytoplasmic-extracellular matrix. As a rapid, reagent-efficient, and high-quality histological staining approach, the presented technique holds great potential for widespread application in the future.
We introduce a unidirectional imager that facilitates polarization-insensitive and broadband operation using isotropic, linear materials. This design comprises diffractive layers with hundreds of thousands of learnable phase features, trained using deep learning to enable power-efficient, high-fidelity imaging in the forward direction (A-to-B), while simultaneously inhibiting optical transmission and image formation in the reverse direction (B-to-A). We experimentally tested our designs using terahertz radiation, providing a good match with our simulations. Furthermore, we demonstrated a wavelength-selective unidirectional imager that performs unidirectional imaging along A-to-B at a predetermined wavelength, while at a second wavelength, the unidirectional operation switches from B-to-A.
We present the first demonstration of unidirectional imaging that permits image formation along only one direction, from an input field-of-view to an output field-of-view, while eliminating optical transmission in the reverse direction. This unidirectional imager is formed by diffractive layers composed of isotropic linear materials spatially-coded with thousands of phase features optimized using deep learning. We experimentally tested our diffractive design using a terahertz setup and 3D-printed diffractive layers, which revealed a good agreement with our numerical simulations. The designs of these diffractive unidirectional imagers are compact and can be scaled to operate at different parts of the electromagnetic spectrum.
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