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Found 10 papers, 1 papers with code
Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
We also demonstrated that this data-driven plaque assay offers the capability of quantifying the infected area of the cell monolayer, performing automated counting and quantification of PFUs and virus-infected areas over a 10-fold larger dynamic range of virus concentration than standard viral plaque assays.
Deep Learning-enabled Detection and Classification of Bacterial Colonies using a Thin Film Transistor (TFT) Image Sensor
Due to the large scalability, ultra-large field-of-view, and low cost of the TFT-based image sensors, this platform can be integrated with each agar plate to be tested and disposed of after the automated CFU count.
Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning
The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis.
Deep learning-based virtual refocusing of images using an engineered point-spread function
We present a virtual image refocusing method over an extended depth of field (DOF) enabled by cascaded neural networks and a double-helix point-spread function (DH-PSF).
Deep learning-based holographic polarization microscopy
Our analysis shows that a trained deep neural network can extract the birefringence information using both the sample specific morphological features as well as the holographic amplitude and phase distribution.
Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning
Our experiments further confirmed that this method successfully detects 90% of bacterial colonies within 7-10 h (and >95% within 12 h) with a precision of 99. 2-100%, and correctly identifies their species in 7. 6-12 h with 80% accuracy.
Cultural Vocal Bursts Intensity Prediction
General Classification
Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning
Three-dimensional (3D) fluorescence microscopy in general requires axial scanning to capture images of a sample at different planes.
Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue
Here, we demonstrate a label-free approach to create a virtually-stained microscopic image using a single wide-field auto-fluorescence image of an unlabeled tissue sample, bypassing the standard histochemical staining process, saving time and cost.
Deep learning enhanced mobile-phone microscopy
Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance.
Deep Learning Microscopy
We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field.
