Search Results for author:
Found 80 papers, 1 papers with code
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.
Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Data
The integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging.
Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
These wavelength-multiplexed patterns are projected onto a single field-of-view (FOV) at the output plane of the diffractive processor, enabling the capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor.
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.
Deep Learning-based Kinetic Analysis in Paper-based Analytical Cartridges Integrated with Field-effect Transistors
This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses.
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.
All-optical complex field imaging using diffractive processors
Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing.
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.
All-Optical Phase Conjugation Using Diffractive Wavefront Processing
Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with various applications ranging from imaging to beam focusing.
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).
All-optical image denoising using a diffractive visual processor
Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%.
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.
Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
Here, we present the design of a diffractive processor that can all-optically perform multispectral quantitative phase imaging of transparent phase-only objects in a snapshot.
Virtual histological staining of unlabeled autopsy tissue
Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time.
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems
Uncertainty estimation is critical for numerous applications of deep neural networks and draws growing attention from researchers.
Learning Diffractive Optical Communication Around Arbitrary Opaque Occlusions
In this scheme, an electronic neural network encoder and a diffractive optical network decoder are jointly trained using deep learning to transfer the optical information or message of interest around the opaque occlusion of an arbitrary shape.
Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
We demonstrate universal polarization transformers based on an engineered diffractive volume, which can synthesize a large set of arbitrarily-selected, complex-valued polarization scattering matrices between the polarization states at different positions within its input and output field-of-views (FOVs).
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.
eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging
The application of deep learning techniques has greatly enhanced holographic imaging capabilities, leading to improved phase recovery and image reconstruction.
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.
Snapshot Multispectral Imaging Using a Diffractive Optical Network
Moreover, we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially-repeating virtual spectral filter array with 2x2=4 unique bands at terahertz spectrum.
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.
Self-supervised learning of hologram reconstruction using physics consistency
Here, we report a self-supervised learning model, termed GedankenNet, that eliminates the need for labeled or experimental training data, and demonstrate its effectiveness and superior generalization on hologram reconstruction tasks.
Virtual impactor-based label-free bio-aerosol detection using holography and deep learning
To address this need, we present a mobile and cost-effective label-free bio-aerosol sensor that takes holographic images of flowing particulate matter concentrated by a virtual impactor, which selectively slows down and guides particles larger than ~6 microns to fly through an imaging window.
Time-lapse image classification using a diffractive neural network
Here we demonstrate, for the first time, a "time-lapse" image classification scheme using a diffractive network, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other.
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.
Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks
Permutation matrices form an important computational building block frequently used in various fields including e. g., communications, information security and data processing.
Super-resolution image display using diffractive decoders
We report a deep learning-enabled diffractive display design that is based on a jointly-trained pair of an electronic encoder and a diffractive optical decoder to synthesize/project super-resolved images using low-resolution wavefront modulators.
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.
Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
Deep learning-based image reconstruction methods have achieved remarkable success in phase recovery and holographic imaging.
Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network
The transmission layers of this polarization multiplexed diffractive network are trained and optimized via deep learning and error-backpropagation by using thousands of examples of the input/output fields corresponding to each one of the complex-valued linear transformations assigned to different input/output polarization combinations.
Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network
Here, we demonstrate a few-shot transfer learning method that helps a holographic image reconstruction deep neural network rapidly generalize to new types of samples using small datasets.
Diffractive all-optical computing for quantitative phase imaging
Quantitative phase imaging (QPI) is a label-free computational imaging technique that provides optical path length information of specimens.
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.
Cascadable all-optical NAND gates using diffractive networks
Based on this architecture, we numerically optimized the design of a diffractive neural network composed of 4 passive layers to all-optically perform NAND operation using the diffraction of light, and cascaded these diffractive NAND gates to perform complex logical functions by successively feeding the output of one diffractive NAND gate into another.
All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces
In addition to this data-free design approach, we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target transformation.
Classification and reconstruction of spatially overlapping phase images using diffractive optical networks
In addition to all-optical classification of overlapping phase objects, we also demonstrate the reconstruction of these phase images based on a shallow electronic neural network that uses the highly compressed output of the diffractive network as its input (with e. g., ~20-65 times less number of pixels) to rapidly reconstruct both of the phase images, despite their spatial overlap and related phase ambiguity.
Dynamic imaging and characterization of volatile aerosols in e-cigarette emissions using deep learning-based holographic microscopy
To evaluate the effects of e-liquid composition on aerosol dynamics, we measured the volatility of the particles generated by flavorless, nicotine-free e-liquids with various PG/VG volumetric ratios, revealing a negative correlation between the particles' volatility and the volumetric ratio of VG in the e-liquid.
Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
Using 2-fold undersampled spectral data (i. e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in ~6. 73 ms using a desktop computer, removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i. e., 1280 spectral points per A-line).
Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks
Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging.
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).
Neural network-based on-chip spectroscopy using a scalable plasmonic encoder
Conventional spectrometers are limited by trade-offs set by size, cost, signal-to-noise ratio (SNR), and spectral resolution.
Scale-, shift- and rotation-invariant diffractive optical networks
Recent research efforts in optical computing have gravitated towards developing optical neural networks that aim to benefit from the processing speed and parallelism of optics/photonics in machine learning applications.
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.
Ensemble learning of diffractive optical networks
A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.
Deep learning-based transformation of the H&E stain into special stains
Based on evaluation by three renal pathologists, followed by adjudication by a fourth renal pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis in several non-neoplastic kidney diseases sampled from 58 unique subjects.
All-Optical Information Processing Capacity of Diffractive Surfaces
Precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics.
Label-free detection of Giardia lamblia cysts using a deep learning-enabled portable imaging flow cytometer
We report a field-portable and cost-effective imaging flow cytometer that uses deep learning to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL/h.
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.
Terahertz Pulse Shaping Using Diffractive Surfaces
Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics.
Misalignment Resilient Diffractive Optical Networks
By modeling the undesired layer-to-layer misalignments in 3D as continuous random variables in the optical forward model, diffractive networks are trained to maintain their inference accuracy over a large range of misalignments; we term this diffractive network design as vaccinated D2NN (v-D2NN).
Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction.
Single-shot autofocusing of microscopy images using deep learning
We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that is acquired at an arbitrary out-of-focus plane.
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
Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue
This approach uses a single deep neural network that receives two different sources of information at its input: (1) autofluorescence images of the label-free tissue sample, and (2) a digital staining matrix which represents the desired microscopic map of different stains to be virtually generated at the same tissue section.
Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks
We report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to all-optically perform a specific task learned using deep learning.
Deep learning-based color holographic microscopy
We report a framework based on a generative adversarial network (GAN) that performs high-fidelity color image reconstruction using a single hologram of a sample that is illuminated simultaneously by light at three different wavelengths.
Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy
Similar to ensemble methods practiced in machine learning, we also independently-optimized multiple differential diffractive networks that optically project their light onto a common detector plane, and achieved testing accuracies of 98. 59%, 91. 06% and 51. 44% for MNIST, Fashion-MNIST and grayscale CIFAR-10, respectively.
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.
Resolution enhancement in scanning electron microscopy using deep learning
We report resolution enhancement in scanning electron microscopy (SEM) images using a generative adversarial network.
Cross-modality deep learning brings bright-field microscopy contrast to holography
Deep learning brings bright-field microscopy contrast to holographic images of a sample volume, bridging the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of bright-field incoherent microscopy.
Deep learning-based super-resolution in coherent imaging systems
We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems.
Response to Comment on "All-optical machine learning using diffractive deep neural networks"
In their Comment, Wei et al. (arXiv:1809. 08360v1 [cs. LG]) claim that our original interpretation of Diffractive Deep Neural Networks (D2NN) represent a mischaracterization of the system due to linearity and passivity.
Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks
Furthermore, we report the integration of D2NNs with electronic neural networks to create hybrid-classifiers that significantly reduce the number of input pixels into an electronic network using an ultra-compact front-end D2NN with a layer-to-layer distance of a few wavelengths, also reducing the complexity of the successive electronic network.
PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning
Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform quantitative phase images (QPI) of labelfree tissue sections into images that are equivalent to brightfield microscopy images of the same samples that are histochemically stained.
Toward a Thinking Microscope: Deep Learning in Optical Microscopy and Image Reconstruction
We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations among different modes and modalities of microscopic imaging, driven entirely by image data.
All-Optical Machine Learning Using Diffractive Deep Neural Networks
We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively.
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.
Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery
Holography encodes the three dimensional (3D) information of a sample in the form of an intensity-only recording.
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.
Phase recovery and holographic image reconstruction using deep learning in neural networks
Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography.
