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Found 37 papers, 1 papers with code
Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning
Gram staining has been one of the most frequently used staining protocols in microbiology for over a century, utilized across various fields, including diagnostics, food safety, and environmental monitoring.
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.
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.
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.
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.
