Nant Holdingsip, LLC

Nantomics, LLC

Nanthealth, Inc.

Nantcell, Inc.

Mustafa Jaber

Bing Song

Jeremi M Sudol

Liudmila A Beziaeva

Christopher W Szeto

Shahrooz Rabizadeh

Stephen Charles Benz

Liwen Lin

A method of determining a region of interest in an image of tissue of an individual by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to partition an image of tissue of an individual into a set of areas, identify a tissue type of each area of the image, and apply a classifier to the image to determine a region of interest, the classifier being configured to determine regions of interest based on the tissue types of the set of areas of the image.

Deep learning models for region of interest determination

A method of determining a clinical value for an individual based on a tumor in an image by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to determine a lymphocyte distribution of lymphocytes in the tumor based on the image; apply a classifier to the lymphocyte distribution to classify the tumor, the classifier having been trained to classify tumors into a class selected from at least two classes respectively associated with lymphocyte distributions; and determine the clinical value for the individual based on prognoses of individuals with tumors in the class into which the classifier classified the tumor.

Deep learning models for tumor evaluation

Techniques are provided for determining classifications based on WSIs. A varied-size feature map is generated for each training WSI by generating a grid of patches for the training WSI, segmenting the training WSI into tissue and non-tissue areas, and converting patches comprising the tissue areas into tensors. Bounding boxes are generated based on the patches comprising tissue areas and segmented into feature map patches. A fixed-size feature map is generated based on a subset of the feature map patches. A classifier model is trained to process fixed-size feature maps corresponding to the training WSIs such that, for each fixed-size feature map, the classifier model is operable to assign a WSI-level tissue or cell morphology classification or regression based on the tensors. A classification engine is configured to use the trained classifier model to determine a WSI-level tissue or cell morphology classification or regression for a test WSI.

Weakly supervised learning with whole slide images

A method at a computing device for classifying elements within an input, the method including breaking the input into a plurality of patches; for each patch: creating a vector output; applying a characterization map to select a classification bin from a plurality of classification bins; and utilizing the selected classification bin to classify the vector output to create a classified output; and compiling the classified output from each patch.

Classification based on characterization analysis methods and systems

Deep learning models for region-of-interest determination

A system capable of determining which recognition algorithms should be applied to regions of interest within digital representations is presented. A preprocessing module utilizes one or more feature identification algorithms to determine regions of interest based on feature density. The preprocessing modules leverages the feature density signature for each region to determine which of a plurality of diverse recognition modules should operate on the region of interest. A specific embodiment that focuses on structured documents is also presented. Further, the disclosed approach can be enhanced by addition of an object classifier that classifies types of objects found in the regions of interest.

Feature density object classification, systems and methods

A computer implemented method of generating at least one shape of a region of interest in a digital image is provided. The method includes obtaining, by an image processing engine, access to a digital tissue image of a biological sample; tiling, by the image processing engine, the digital tissue image into a collection of image patches; obtaining, by the image processing engine, a plurality of features from each patch in the collection of image patches, the plurality of features defining a patch feature vector in a multidimensional feature space including the plurality of features as dimensions; determining, by the image processing engine, a user selection of a user selected subset of patches in the collection of image patches; classifying, by applying a trained classifier to patch vectors of other patches in the collection of patches, the other patches as belonging or not belonging to a same class of interest as the user selected subset of patches; and identifying one or more regions of interest based at least in part on the results of the classifying.

Few-shot learning based image recognition of whole slide image at tissue level

Techniques are provided that include identifying robust features within a training image. Training features are generated by applying a feature detection algorithm to the training image, each training feature having a training feature location within the training image. At least a portion of the training image is transformed into a transformed image in accordance with a predefined image transformation. Transform features are generated by applying the feature detection algorithm to the transformed image, each transform feature having a transform feature location within the transformed image. The training feature locations of the training features are mapped to corresponding training feature transformed locations within the transformed image in accordance with the predefined image transformation, and a robust feature set is compiled by selecting robust features, wherein each robust feature represents a training feature having a training feature transformed location proximal to a transform feature location of one of the transform features.

Robust feature identification for image-based object recognition

Systems and methods of verifying the results of an initial image recognition process are presented. A verification engine can receive a set of candidate images corresponding to the results of an image recognition process performed on a captured query image. The verification engine can determine an appropriate verification technique to apply to the images of the candidate set, and classify, re-rank or otherwise re-organize the candidate set such that the best match from the candidate set is confirmed as a proper match.

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Los Angeles, CA, United States of America

Mustafa Jaber