Described herein are systems, methods, and programming for analyzing and classifying digital pathology images. Some embodiments include receiving whole slide images (WSIs) and dividing each of the WSIs into tiles. For each WSI, a random subset of the tiles may be selected and augmented views of each of the selected tiles may be generated. For each of the selected tiles, a first convolutional neural network (CNN) may be trained to: generate, using a first one of the augmented views corresponding to the selected tile, a first representation of the selected tile, and predict a second representation of the selected tile to be generated by a second CNN, wherein the second representation is generated based on a second one of the augmented views of the selected tile.

Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, determining a quality control (QC) machine learning model to predict a quality designation based on one or more artifacts, providing the digital image as an input to the QC machine learning model, receiving the quality designation for the digital image as an output from the machine learning model, and outputting the quality designation of the digital image. A quality assurance (QA) machine learning model may predict a disease designation based on one or more biomarkers. The digital image may be provided to the QA model which may output a disease designation. An external designation may be compared to the disease designation and a comparison result may be output.

Various examples include systems, apparatuses, and methods to perform an automated visual-inspection of components undergoing various stages of fabrication. In one example, an inspection system includes a number of robots, each having a camera, to inspect a component for defects at various stages of fabrication. Generally, each of the cameras is located at a different geographical location corresponding to the various stages in the fabrication of the component. At least some of the cameras are arranged to inspect all surfaces of the component that are not facing a table upon which the component is mounted. The system also includes a respective data-collection station electronically coupled to each the number of robots and an associated one of the cameras. A master data-collection station is electronically coupled to each of the data-collection stations. Other systems, apparatuses, and methods are disclosed.

A method for matching a three-dimensional first image of at least one discrete entity with a three-dimensional second image of the at least one discrete entity is provided. The at least one discrete entity includes a biological sample and a plurality of constituent parts of a marker. The method includes: generating a first representation of the marker from the first image; generating a second representation of the marker from the second image; and based upon the representations matching, matching the first image with the second image; or based upon the representations not matching, rejecting the match. Generating the representations includes determining vectors from at least one reference item to at least some of the constituent parts of the marker, determining for the vectors at least one value of a property, and generating the representations of the marker based on a frequency of the at least one value of the property.

A method, a computer program product, and a computer system for classifying bacteria. The method comprises extracting a morphology signature corresponding to one or more bacteria and extracting a motility signature corresponding to the one or more bacteria. The method further comprises merging the morphology signature and the motility signature into a merged vector signature and classifying the one or more bacteria based on the merged vector signature.

A method for processing electronic images using uncertainty estimation may be used to determine whether to use an artificial intelligence (AI) assisted prediction. The method may include receiving one or more electronic images associated with a pathology specimen and providing the one or more electronic images to a machine learning model. The machine learning model may perform operations including determining a certainty level corresponding to a certainty that a predetermined AI system will provide an accurate prediction, determining whether the certainty level equals or exceeds a predetermined confidence threshold, and, upon determining that the certainty level does not equal or exceed a predetermined confidence threshold, determining to not use the predetermined AI system.

An imaging device for producing images of a scene, the imaging device comprising: a first and a second hyperchromatic lens being arranged in a stereoscopic configuration to receive light from the scene; image sensor circuitry configured to capture a first and second image of the light encountered by the first and the second lens respectively; processor circuitry configured to: produce depth information using the captured first and second images of the scene and produce a resultant first and second image of the scene using both the captured first and second image and the depth information.

A computer-implemented method for processing electronic medical images, the method including receiving a plurality of electronic medical images of a medical specimen. Each of the plurality of electronic medical images may be divided into a plurality of tiles. A plurality of sets of matching tiles may be determined, the tiles within each set corresponding to a given region of a plurality of regions of the medical specimen. For each tile of the plurality of sets of matching tiles, a blur score may be determined corresponding to a level of image blur of the tile. For each set of matching tiles, a tile may be determined with the blur score indicating the lowest level of blur. A composite electronic medical image, comprising a plurality of tiles from each set of matching tiles with the blur score indicating the lowest level of blur, may be determined and provided for display.

The technology disclosed present systems and methods to produce an enhanced resolution image from images of a target using structured illumination microscopy (SIM). The method includes transforming at least three images of the target captured by a sensor in a spatial domain into a Fourier domain to produce at least three frequency domain matrices that each include first blocks of complex coefficients and redundant second blocks of complex coefficients that are conjugates to the first blocks. The method includes reducing computing resources required to produce the enhanced resolution image by using first blocks of complex coefficients to produce at least three phase-separated half-matrices in the Fourier domain. The method includes performing one or more intermediate transformation on the phase-separated half-matrices to produce realigned shifted half-matrices. The method includes calculating complex coefficients of second blocks in the Fourier domain to produce full matrices from half-matrices.

A device includes image generation circuitry and convolutional-neural-network circuitry. The image generation circuitry, in operation, generates a digital image representation of a wafer defect map (WDM). The convolutional-neural-network circuitry, in operation, generates a defect classification associated with the WDM based on: the digital image representation of the WDM and a data-driven model associating WDM images with classes of a defined set of classes of wafer defects and generated using a training data set augmented based on defect pattern orientation types associated with training images.