Autofocusing technologies for whole slide imaging and automated microscopy
Whole slide imaging (WSI) has moved digital pathology closer to diagnostic practice in
recent years. Due to the inherent tissue topography variability, accurate autofocusing …
recent years. Due to the inherent tissue topography variability, accurate autofocusing …
Neural network-based processing and reconstruction of compromised biophotonic image data
In recent years, the integration of deep learning techniques with biophotonic setups has
opened new horizons in bioimaging. A compelling trend in this field involves deliberately …
opened new horizons in bioimaging. A compelling trend in this field involves deliberately …
[HTML][HTML] Self-supervised learning of hologram reconstruction using physics consistency
Existing applications of deep learning in computational imaging and microscopy mostly
depend on supervised learning, requiring large-scale, diverse and labelled training data …
depend on supervised learning, requiring large-scale, diverse and labelled training data …
Deep imaging flow cytometry
Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications
by virtue of its ability to image single cells in a high-throughput manner. However, there …
by virtue of its ability to image single cells in a high-throughput manner. However, there …
Identification of microplastics based on the fractal properties of their holographic fingerprint
Water plastic pollution is a serious problem affecting sealife, marine habitats, and the food
chain. Artificial intelligence-enabled coherent imaging has recently shown exciting …
chain. Artificial intelligence-enabled coherent imaging has recently shown exciting …
Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
A plaque assay—the gold-standard method for measuring the concentration of replication-
competent lytic virions—requires staining and usually more than 48 h of runtime. Here we …
competent lytic virions—requires staining and usually more than 48 h of runtime. Here we …
Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy
Light-sheet fluorescence microscopy (LSFM) is a minimally invasive and high throughput
imaging technique ideal for capturing large volumes of tissue with sub-cellular resolution. A …
imaging technique ideal for capturing large volumes of tissue with sub-cellular resolution. A …
GANscan: continuous scanning microscopy using deep learning deblurring
Most whole slide imaging (WSI) systems today rely on the “stop-and-stare” approach, where,
at each field of view, the scanning stage is brought to a complete stop before the camera …
at each field of view, the scanning stage is brought to a complete stop before the camera …
Deep learning-based single-shot autofocus method for digital microscopy
Digital pathology is being transformed by artificial intelligence (AI)-based pathological
diagnosis. One major challenge for correct AI diagnoses is to ensure the focus quality of …
diagnosis. One major challenge for correct AI diagnoses is to ensure the focus quality of …
Recurrent neural network-based volumetric fluorescence microscopy
Volumetric imaging of samples using fluorescence microscopy plays an important role in
various fields including physical, medical and life sciences. Here we report a deep learning …
various fields including physical, medical and life sciences. Here we report a deep learning …