A SqueezeNet CNN classifies immune cells from label-free multiphoton autofluorescence images, achieving 0.89 ROC-AUC for binary classification and 0.689 F1 for six-class tasks.
Cancer cell classification based on morphological features of 3d phase contrast microscopy using deep neural network,
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Detecting immune cells with label-free two-photon autofluorescence and deep learning
A SqueezeNet CNN classifies immune cells from label-free multiphoton autofluorescence images, achieving 0.89 ROC-AUC for binary classification and 0.689 F1 for six-class tasks.