- The reduction of the size and cost of microscopes creates new possibilities for disease screening and health care in resource-limited settings. Smartphones and mobile devices can be useful for the construction of compact and portable imaging devices, by replacing expensive and bulky optical instruments with state-of-the-art cameras, high-performance processors, and network connectivity in a compact footprint. -
-- We propose a smartphone-mountable computational microscope that achieves wide-field and high-resolution imaging, based on the Fourier ptychographic (FP) microscopy technique. Our device uses the smartphone’s built-in camera for microscopy imaging, the display screen for programmable illumination, and the computational power of the smartphone processors for FP image reconstruction. -
-- With a compact and lightweight optics module and a custom-built application, we can transform a regular smartphone into a high-performance microscope device with a space-bandwidth product exceeding that of regular benchtop microscopes. Our device achieves a half-pitch resolution of 870 nm over a wide field-of-view of 2.1 × 1.6 $mm^2$ with amplitude, phase, and color imaging capabilities. -
-- As global health issues continue to persist and new epidemic challenges arise, we believe that our smartphone microscopy technology can effectively address the clinical needs in point-of-care environments. + The reduction of the size and cost of microscopes creates new possibilities for disease screening and health care in resource-limited settings. Smartphones and mobile devices can be useful for the construction of compact and portable imaging devices, by replacing expensive and bulky optical instruments with state-of-the-art cameras, high-performance processors, and network connectivity in a compact footprint. We propose a smartphone-mountable computational microscope that achieves wide-field and high-resolution imaging, based on the Fourier ptychographic (FP) microscopy technique. Our device uses the smartphone’s built-in camera for microscopy imaging, the display screen for programmable illumination, and the computational power of the smartphone processors for FP image reconstruction. With a compact and lightweight optics module and a custom-built application, we can transform a regular smartphone into a high-performance microscope device with a space-bandwidth product exceeding that of regular benchtop microscopes. Our device achieves a half-pitch resolution of 870 nm over a wide field-of-view of 2.1 × 1.6 $mm^2$ with amplitude, phase, and color imaging capabilities. As global health issues continue to persist and new epidemic challenges arise, we believe that our smartphone microscopy technology can effectively address the clinical needs in point-of-care environments.
+- Virtual staining techniques now enable the transformation of label-free images into clinically standard stained images; however, the extensive costs and time requirements of labelled dataset generation impede its widespread use in clinical settings. -
-- We aim to implement semi-supervised learning for virtual staining to enable high-fidelity results while reducing the need for labor-intensive dataset generation. To achieve this, we designed a neural network architecture that digitally stains label-free images of unstained tissue by minimizing style feature differences between the outputs of stained and unstained raw images. -
-- We have compared the output from the virtual staining network with various training strategy including supervised training with end to end and cascaded architecture, unsupervised training, and our semi-supervised training. Additionally, for consistency in comparison, we prepared a unstained slide and a well-stained slide from the same tissue block. While minor differences in cellular features may exist between the slides, our methods exhibits the best performance among the methods as well as their overall tissue structures remain comparable. -
-- Our approach is particularly valuable for computational microscopy techniques that enable various label-free imaging applications, where generating paired datasets is challenging, such as in 3D pathology and immunohistochemistry staining. + Virtual staining techniques now enable the transformation of label-free images into clinically standard stained images; however, the extensive costs and time requirements of labelled dataset generation impede its widespread use in clinical settings. We aim to implement semi-supervised learning for virtual staining to enable high-fidelity results while reducing the need for labor-intensive dataset generation. To achieve this, we designed a neural network architecture that digitally stains label-free images of unstained tissue by minimizing style feature differences between the outputs of stained and unstained raw images. We have compared the output from the virtual staining network with various training strategy including supervised training with end to end and cascaded architecture, unsupervised training, and our semi-supervised training. Additionally, for consistency in comparison, we prepared a unstained slide and a well-stained slide from the same tissue block. While minor differences in cellular features may exist between the slides, our methods exhibits the best performance among the methods as well as their overall tissue structures remain comparable. Our approach is particularly valuable for computational microscopy techniques that enable various label-free imaging applications, where generating paired datasets is challenging, such as in 3D pathology and immunohistochemistry staining.
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