Abstract: A system for the detection and classification of live microorganisms in a sample includes a light source and an incubator holding one or more sample-containing growth plates. A translation stage moves the image sensor and/or the growth plate(s) along one or more dimensions to capture time-lapse holographic images of microorganisms. Image processing software executed by a computing device captures time-lapse holographic images of the microorganisms or clusters of microorganisms on the one or more growth plates. The image processing software is configured to detect candidate microorganism colonies in reconstructed, time-lapse holographic images based on differential image analysis. The image processing software includes one or more trained deep neural networks that process the time-lapsed image(s) of candidate microorganism colonies to detect true microorganism colonies and/or output a species associated with each true microorganism colony.

Abstract: A deep learning-based virtual HER2 IHC staining method uses a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this staining framework was demonstrated by quantitative analysis of blindly graded HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs). A second quantitative blinded study revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts.

Abstract: A method for refining virtually-stained image annotations. The method includes separating an image of a stained tissue section into an image of a counterstain and an image of immunostaining visual signals via linear transformation. The method includes identifying and segmenting nuclei in the image of the counterstain to produce an image of a refined counterstain with single-cell segments. The method includes comparing immunostaining visual signals in the single-cell segments of the image of the refined counterstain to a user-defined threshold to determine true positive cells. The method includes refining the image of the immunostaining visual signals to produce an image of refined immunostaining visual signals. The refining is based on the stained tissue section protein localization or morphology and the determined true positive cells. The method includes overlaying the image of the counterstain and the image of refined immunostaining visual signals to produce a refined virtually-stained image annotation.

Abstract: A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.

Abstract: A deep learning-based digital staining method and system are disclosed that provides a label-free approach to create a virtually-stained microscopic images from quantitative phase images (QPI) of label-free samples. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses a convolutional neural network trained using a generative adversarial network model to transform QPI images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample. This label-free digital staining method eliminates cumbersome and costly histochemical staining procedures, and would significantly simplify tissue preparation in pathology and histology fields.

Abstract: Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging. Recovery of the missing phase information of a hologram is an important step in holographic image reconstruction. A convolutional recurrent neural network (RNN)-based phase recovery approach is employed that uses multiple holograms, captured at different sample-to-sensor distances to rapidly reconstruct the phase and amplitude information of a sample, while also performing autofocusing through the same trained neural network. The success of this deep learning-enabled holography method is demonstrated by imaging microscopic features of human tissue samples and Papanicolaou (Pap) smears. These results constitute the first demonstration of the use of recurrent neural networks for holographic imaging and phase recovery, and compared with existing methods, the presented approach improves the reconstructed image quality, while also increasing the depth-of-field and inference speed.

Abstract: An all-optical Diffractive Deep Neural Network (D2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs. In alternative embodiments, the all-optical D2NN is used as a front-end in conjunction with a trained, digital neural network back-end.

Abstract: A computer-free system and method is disclosed that uses an all-optical image reconstruction method to see through random diffusers at the speed of light. Using deep learning, a set of transmissive layers are trained to all-optically reconstruct images of arbitrary objects that are distorted by random phase diffusers. After the training stage, the resulting diffractive layers are fabricated and form a diffractive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown, new phase diffuser. Unlike digital methods, all-optical diffractive reconstructions do not require power except for the illumination light. This diffractive solution to see through diffusive and/or scattering media can be extended to other wavelengths, and can fuel various applications in biomedical imaging, astronomy, atmospheric sciences, oceanography, security, robotics, autonomous vehicles, among many others.

Abstract: A deep learning-based system and method is provided that uses a convolutional neural network to rapidly transform in vivo reflectance confocal microscopy (RCM) images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of epidermis, dermal-epidermal junction, and superficial dermis layers. The network is trained using ex vivo RCM images of excised unstained tissue and microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. The trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features of traditional histology from the same excised tissue. The system and method enables more rapid diagnosis of malignant skin neoplasms and reduces invasive skin biopsies.

Abstract: A trained deep neural network transforms an image of a sample obtained with a holographic microscope to an image that substantially resembles a microscopy image obtained with a microscope having a different microscopy image modality. Examples of different imaging modalities include bright-field, fluorescence, and dark-field. For bright-field applications, deep learning brings bright-field microscopy contrast to holographic images of a sample, bridging the volumetric imaging capability of holography with the speckle-free and artifact-free image contrast of bright-field microscopy.

Abstract: A deep learning-based digital staining method and system are disclosed that enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally/virtually-stained whole slide images (WSIs) of unlabeled/unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in one embodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample.

Abstract: A fluorescence microscopy method includes a trained deep neural network. At least one 2D fluorescence microscopy image of a sample is input to the trained deep neural network, wherein the input image(s) is appended with a digital propagation matrix (DPM) that represents, pixel-by-pixel, an axial distance of a user-defined or automatically generated surface within the sample from a plane of the input image. The trained deep neural network outputs fluorescence output image(s) of the sample that is digitally propagated or refocused to the user-defined surface or automatically generated. The method and system cross-connects different imaging modalities, permitting 3D propagation of wide-field fluorescence image(s) to match confocal microscopy images at different sample planes. The method may be used to output a time sequence of images (e.g., time-lapse video) of a 2D or 3D surface within a sample.

Abstract: A deep learning-based volumetric image inference system and method are disclosed that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume. Through a recurrent convolutional neural network (RNN) (referred to herein as Recurrent-MZ), 2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field. Using experiments on C. elegans and nanobead samples, Recurrent-MZ is demonstrated to increase the depth-of-field of a 63×/1.4 NA objective lens by approximately 50-fold, also providing a 30-fold reduction in the number of axial scans required to image the same sample volume. The generalization of this recurrent network for 3D imaging is further demonstrated by showing its resilience to varying imaging conditions, including e.g.

Abstract: A deep learning-based digital staining method and system are disclosed that enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally/virtually-stained whole slide images (WSIs) of unlabeled/unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in one embodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample.

Abstract: A method of forming an optical neural network for processing an input object image or optical signal that is invariant to object transformations includes training a software-based neural network model to perform one or more specific optical functions for a multi-layer optical network having physical features located in each of the layers of the optical neural network. The training includes feeding different input object images or optical signals that have random transformations or shifts and computing at least one optical output of optical transmission and/or reflection through the optical neural network using an optical wave propagation model and iteratively adjusting transmission/reflection coefficients for each layer until optimized transmission/reflection coefficients are obtained.

Abstract: An all-optical Diffractive Deep Neural Network (D2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs. In alternative embodiments, the all-optical D2NN is used as a front-end in conjunction with a trained, digital neural network back-end.

Abstract: A method of generating virtually-stained image annotations. The method includes providing a neural network executed by image processing software. The image processing software runs on a processor of a computing device. The method includes training the neural network with a plurality of chemically stained patterns of endogenous signals to identify virtual staining patterns. The method includes producing and annotating an image of a biological sample for a biomarker. The biological sample includes endogenous signals. The method includes identifying, using the trained neural network, the virtual staining patterns in the image of the biological sample. Lastly, the method includes overlaying the virtual staining patterns in the image of the biological sample with annotations using spatial matching to produce virtually-stained image annotations.

Abstract: A diffractive network is disclosed that utilizes, in some embodiments, diffractive elements, which are used to shape an arbitrary broadband pulse into a desired optical waveform, forming a compact and passive pulse engineering system. The diffractive network was experimentally shown to generate various different pulses by designing passive diffractive layers that collectively engineer the temporal waveform of an input terahertz pulse. The results constitute the first demonstration of direct pulse shaping in terahertz spectrum, where the amplitude and phase of the input wavelengths are independently controlled through a passive diffractive device, without the need for an external pump. Furthermore, a modular physical transfer learning approach is presented to illustrate pulse-width tunability by replacing part of an existing diffractive network with newly trained diffractive layers, demonstrating its modularity. This learning-based diffractive pulse engineering framework can find broad applications in e.g.

Abstract: An all-optical Diffractive Deep Neural Network (D2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs. In alternative embodiments, the all-optical D2NN is used as a front-end in conjunction with a trained, digital neural network back-end.

Abstract: A machine vision task, machine learning task, and/or classification of objects is performed using a diffractive optical neural network device. Light from objects passes through or reflects off the diffractive optical neural network device formed by multiple substrate layers. The diffractive optical neural network device defines a trained function between an input optical signal from the object light illuminated at a plurality or a continuum of wavelengths and an output optical signal corresponding to one or more unique wavelengths or sets of wavelengths assigned to represent distinct data classes or object types/classes created by optical diffraction and/or reflection through/off the substrate layers. Output light is captured with detector(s) that generate a signal or data that comprise the one or more unique wavelengths or sets of wavelengths assigned to represent distinct data classes or object types or object classes which are used to perform the task or classification.