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Cervical cancer is a high‐risk disease that threatens women's health globally. In this study, we developed the multi‐modal static cytometry that adopted different features to classify the typical human cervical epithelial cells (H8) and cervical cancer cells (HeLa). With the light‐sheet static cytometry, we obtain brightfield (BF) images, fluorescence (FL) images and two‐dimensional (2D) light scattering...
Cervical cancer is a major gynecological malignant tumor that threatens women's health. Current cytological methods have certain limitations for cervical cancer early screening. Light scattering patterns can reflect small differences in the internal structure of cells. In this study, we develop a light scattering pattern specific convolutional network (LSPS‐net) based on deep learning algorithm and...
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