Lung cancer diagnosis with quantitative DIC microscopy and a deep convolutional neural network.

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Abstract

We present a study on lung squamous cell carcinoma diagnosis using quantitative TI-DIC microscopy and a deep convolutional neural network (DCNN). The 2-D phase map of unstained tissue sections is first retrieved from through-focus differential interference contrast (DIC) images based on the transport of intensity equation (TIE). The spatially resolved optical properties are then computed from the 2-D phase map via the scattering-phase theorem. The scattering coefficient (

μ
S

) and the reduced scattering coefficient (

μ
S
‘

) are found to increase whereas the anisotropy factor (g) is found to decrease with cancer. A DCNN classifier is developed afterwards to classify the tissue using either the DIC images or 2-D optical property maps of

μ
S

,

μ
S
‘

and g. The DCNN classifier with the optical property maps exhibits high accuracy, significantly outperforming the same DCNN classifier on the DIC images. The label-free quantitative phase microscopy together with deep learning may emerge as a promising approach for in situ rapid cancer diagnosis.