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Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling
Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis.
Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning
Systemic amyloidosis is a group of diseases characterized by the deposition of misfolded proteins in various organs and tissues, leading to progressive organ dysfunction and failure.
Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network
We demonstrated the feasibility of this reconfigurable multiplexed diffractive design by approximating 256 randomly selected permutation matrices using K=4 rotatable diffractive layers.
Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor
To resolve subwavelength features of an object, the diffractive imager uses a thin, high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder, which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through air.
Information hiding cameras: optical concealment of object information into ordinary images
This information hiding transformation is valid for infinitely many combinations of secret messages, all of which are transformed into ordinary-looking output patterns, achieved all-optically through passive light-matter interactions within the optical processor.
Auto-ICell: An Accessible and Cost-Effective Integrative Droplet Microfluidic System for Real-Time Single-Cell Morphological and Apoptotic Analysis
The Auto-ICell system, a novel, and cost-effective integrated droplet microfluidic system, is introduced for real-time analysis of single-cell morphology and apoptosis.
Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks
Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are non-negative, acting on diffraction-limited optical intensity patterns at the input field-of-view (FOV).
Pyramid diffractive optical networks for unidirectional magnification and demagnification
Our analyses revealed the efficacy of this P-D2NN design in unidirectional image magnification and demagnification tasks, producing high-fidelity magnified or demagnified images in only one direction, while inhibiting the image formation in the opposite direction - confirming the desired unidirectional imaging operation.
Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors
Under spatially-coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number (N) of optimizable phase-only diffractive features is greater than or equal to ~2 Ni x No, where Ni and No refer to the number of useful pixels at the input and the output FOVs, respectively.
Data class-specific all-optical transformations and encryption
We numerically demonstrated all-optical class-specific transformations covering A-->A, I-->I, and P-->I transformations using various image datasets.
Unidirectional Imaging using Deep Learning-Designed Materials
A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) A to an output FOV B, and in the reverse path, the image formation would be blocked.
Deep Learning-enabled Virtual Histological Staining of Biological Samples
Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue.
Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network
We report deep learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily-selected, complex-valued linear transformations between an input and output field-of-view, each with N_i and N_o pixels, respectively.
All-optical image classification through unknown random diffusers using a single-pixel diffractive network
Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields.
Virtual stain transfer in histology via cascaded deep neural networks
Unlike a single neural network structure which only takes one stain type as input to digitally output images of another stain type, C-DNN first uses virtual staining to transform autofluorescence microscopy images into H&E and then performs stain transfer from H&E to the domain of the other stain in a cascaded manner.
Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks
Here, we introduce a fast virtual staining framework that can stain defocused autofluorescence images of unlabeled tissue, achieving equivalent performance to virtual staining of in-focus label-free images, also saving significant imaging time by lowering the microscope's autofocusing precision.
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.
To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects
Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects.
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.
Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers
During its training, random diffusers with a range of correlation lengths were used to improve the diffractive network's generalization performance.
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 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
