Abstract: There is provided a system for computing a single-label neural network for detection of an indication of an acute medical condition, comprising: hardware processor(s) executing a code for: providing a multi-label training dataset including anatomical images each associated with a label indicative of visual finding type(s), or indicative of no visual finding types, training a multi-label neural network for detection of the visual finding types(s) in a target anatomical image according to the multi-label training dataset, creating a single-label training dataset including anatomical images each associated with a label indicative of the selected single visual finding type, or indicative of an absence of the single visual finding type, and training a single-label neural network for detection of the single visual finding type, by setting the trained multi-label neural network as an initial baseline of the single-label neural network, and fine-tuning and/or re-training the baseline according to the single-label tra

Abstract: A system for prioritizing patients for treatment, comprising: at least one hardware processor executing a code for: feeding anatomical images into a visual filter neural network for outputting a category indicative of a target body region depicted at a target sensor orientation and a rotation relative to a baseline, rejecting a sub-set of anatomical images classified into another category, rotating to the baseline images classified as rotated, identifying pixels for each image having outlier pixel intensity values denoting an injection of content, adjusting the outlier pixel intensity values to values computed as a function of non-outlier pixel intensity values, feeding each the remaining sub-set of images with adjusted outlier pixel intensity values into a classification neural network for detecting the visual finding type, generating instructions for creating a triage list for which the classification neural network detected the indication, wherein patients are selected for treatment based on the triage lis

Abstract: Systems and methods are disclosed for focusing a microscope using images acquired under multiple illumination conditions. In one implementation, an autofocus microscope may include an image capture device, a focus actuator, an illumination assembly, and a controller. The controller may cause the illumination assembly to illuminate a sample at a first illumination condition and at a second illumination condition. The controller may acquire a first image of the sample illuminated from the first illumination angle and a second image of the sample illuminated from the second illumination angle. The controller may further determine an amount of shift between image features present in the first image of the sample and a corresponding image features present in the second image of the sample, If the amount of determined shift is non-zero, the focus actuator may change the distance between the sample and the focal plane.

Abstract: Methods and systems are provided for improved imaging and analyzing of a sample with a large field-of-view at a high image resolution. A diagnostic system may comprise: a microscope comprising a low collection numerical aperture (NA); an imaging device coupled to the microscope; and a processor coupled to the imaging device. The imaging device may be configured to capture a plurality of low-resolution images of a region of a sample viewed by the microscope. The region of the sample may comprise cells. The processor may comprise instructions configured to reconstruct a high-resolution image of the region of the sample using the plurality of low-resolution images. The processor may further comprise instructions configured to analyze a spatial field of the high-resolution image to identify at least one of a cell type or a cell structure of at least one of the cells of the region of the sample.

Abstract: A system for prioritizing patients for treatment, comprising: at least one hardware processor executing a code for: feeding anatomical images into a visual filter neural network for outputting a category indicative of a target body region depicted at a target sensor orientation and a rotation relative to a baseline, rejecting a sub-set of anatomical images classified into another category, rotating to the baseline images classified as rotated, identifying pixels for each image having outlier pixel intensity values denoting an injection of content, adjusting the outlier pixel intensity values to values computed as a function of non-outlier pixel intensity values, feeding each the remaining sub-set of images with adjusted outlier pixel intensity values into a classification neural network for detecting the visual finding type, generating instructions for creating a triage list for which the classification neural network detected the indication, wherein patients are selected for treatment based on the triage lis

Abstract: There is provided a system for computing a single-label neural network for detection of an indication of an acute medical condition, comprising: hardware processor(s) executing a code for: providing a multi-label training dataset including anatomical images each associated with a label indicative of visual finding type(s), or indicative of no visual finding types, training a multi-label neural network for detection of the visual finding types(s) in a target anatomical image according to the multi-label training dataset, creating a single-label training dataset including anatomical images each associated with a label indicative of the selected single visual finding type, or indicative of an absence of the single visual finding type, and training a single-label neural network for detection of the single visual finding type, by setting the trained multi-label neural network as an initial baseline of the single-label neural network, and fine-tuning and/or re-training the baseline according to the single-label tra

Abstract: Systems and methods are disclosed for focusing a microscope using images acquired under multiple illumination conditions. In one implementation, an autofocus microscope may include an image capture device, a focus actuator, an illumination assembly, and a controller. The controller may cause the illumination assembly to illuminate a sample at a first illumination condition and at a second illumination condition. The controller may acquire a first image of the sample illuminated from the first illumination angle and a second image of the sample illuminated from the second illumination angle. The controller may further determine an amount of shift between image features present in the first image of the sample and a corresponding image features present in the second image of the sample, If the amount of determined shift is non-zero, the focus actuator may change the distance between the sample and the focal plane.

Abstract: A method and apparatus for finding correspondence between portions of two images that first subjects the two images to segmentation by weighted aggregation (10), then constructs directed acylic graphs (16,18) from the output of the segmentation by weighted aggregation to obtain hierarchical graphs of aggregates (20,22), and finally applies a maximally weighted subgraph isomorphism to the hierarchical graphs of aggregates to find matches between them (24). Two algorithms are described; one seeks a one-to-one matching between regions, and the other computes a soft matching, in which is an aggregate may have more than one corresponding aggregate. A method and apparatus for image segmentation based on motion cues. Motion provides a strong cue for segmentation. The method begins with local, ambiguous optical flow measurements. It uses a process of aggregation to resolve the ambiguities and reach reliable estimates of the motion.

Abstract: A method and apparatus for finding correspondence between portions of two images that first subjects the two images to segmentation by weighted aggregation (10), then constructs directed acylic graphs (16,18) from the output of the segmentation by weighted aggregation to obtain hierarchical graphs of aggregates (20,22), and finally applies a maximally weighted subgraph isomorphism to the hierarchical graphs of aggregates to find matches between them (24). Two algorithms are described; one seeks a one-to-one matching between regions, and the other computes a soft matching, in which is an aggregate may have more than one corresponding aggregate. A method and apparatus for image segmentation based on motion cues. Motion provides a strong cue for segmentation. The method begins with local, ambiguous optical flow measurements. It uses a process of aggregation to resolve the ambiguities and reach reliable estimates of the motion.