Zebra Medical Vision Ltd.

Yeda Research and Develpment & Co. Ltd

Scopio Labs Ltd.

Chen Brestel

Eli Goz

Jonathan Laserson

Eran Small

Ronen Ezra Basri

Meirav Galun

Alexander Apartsin

Ittay Madar

Erez Naaman

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 training dataset.

Systems and methods for detecting an indication of a visual finding type in an anatomical image

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 list.

Systems and methods for pre-processing anatomical images for feeding into a classification neural network

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.

Autofocus system for a computational microscope

A method and apparatus for finding correspondence between portions of two images that first subjects the two images to segmentation by weighted aggregation (), then constructs directed acylic graphs () from the output of the segmentation by weighted aggregation to obtain hierarchical graphs of aggregates (), and finally applies a maximally weighted subgraph isomorphism to the hierarchical graphs of aggregates to find matches between them (). 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. In addition, as the process of aggregation proceeds and larger aggregates are identified, it employs a progressively more complex model to describe the motion. In particular, the method proceeds by recovering translational motion at fine levels, through affine transformation at intermediate levels, to 3D motion (described by a fundamental matrix) at the coarsest levels. Finally, the method is integrated with a segmentation method that uses intensity cues. The utility of the method is demonstrated on both random dot and real motion sequences.

Method and apparatus for matching portions of input images

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Rehovot, Israel

Chen Brestel