AI Article Synopsis
- The trade-off between high-quality imaging and cellular health poses a significant challenge in optical bioimaging.
- A deep-learning-based power-enhancement (PE) model can generate high-power images from low-power inputs, minimizing the risk of harming cells during the imaging process.
- This PE model, originally trained on normal skin data, can also accurately predict and assist in identifying abnormal skin conditions, such as cancer cells, suggesting its broad applications in both in-vivo and ex-vivo imaging.
Article Abstract
The trade-off between high-quality images and cellular health in optical bioimaging is a crucial problem. We demonstrated a deep-learning-based power-enhancement (PE) model in a harmonic generation microscope (HGM), including second harmonic generation (SHG) and third harmonic generation (THG). Our model can predict high-power HGM images from low-power images, greatly reducing the risk of phototoxicity and photodamage. Furthermore, the PE model trained only on normal skin data can also be used to predict abnormal skin data, enabling the dermatopathologist to successfully identify and label cancer cells. The PE model shows potential for in-vivo and ex-vivo HGM imaging.
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| http://dx.doi.org/10.1002/jbio.202300285 | DOI Listing |
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