SIGNIFICANCE: Fourier ptychography (FP) is a computational imaging approach that achieves high-resolution reconstruction. Inspired by neural networks, many deep-learning-based methods are proposed to solve FP problems. However, the performance of FP ...
SIGNIFICANCE: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell le...
SIGNIFICANCE: We demonstrated the potential of using domain adaptation on functional near-infrared spectroscopy (fNIRS) data to classify different levels of n-back tasks that involve working memory.
SIGNIFICANCE: Spatial frequency-domain imaging (SFDI) is a powerful technique for mapping tissue oxygen saturation over a wide field of view. However, current SFDI methods either require a sequence of several images with different illumination patter...
SIGNIFICANCE: Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transpa...
SIGNIFICANCE: Diffuse reflectance spectroscopy (DRS) is frequently used to assess oxygen saturation and hemoglobin concentration in living tissue. Methods solving the inverse problem may include time-consuming nonlinear optimization or artificial neu...
SIGNIFICANCE: Reducing the bit depth is an effective approach to lower the cost of an optical coherence tomography (OCT) imaging device and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit depth will lead ...
SIGNIFICANCE: Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase im...
SIGNIFICANCE: Researchers have made great progress in single-image super-resolution (SISR) using deep convolutional neural networks. However, in the field of leukocyte imaging, the performance of existing SISR methods is still limited as it fails to ...
SIGNIFICANCE: Our study introduces an application of deep learning to virtually generate fluorescence images to reduce the burdens of cost and time from considerable effort in sample preparation related to chemical fixation and staining.