Paige.ai, Inc.

Patricia Raciti

Christopher Kanan

Leo John Grady

Thomas Fuchs

Belma Dogdas

Alican Bozkurt

Razik Yousfi

Jillian Sue

Rodrigo Ceballos Lentini

Brandon Rothrock

Matthew Hanna

Matthew Houliston

Matthew Lee

Danielle Gorton

Antoine Sainson

Jorge Sergio Reis-Filho

Jeremy Daniel Kunz

Jorge S Reis-filho

Systems and methods are disclosed for identifying formerly conjoined pieces of tissue in a specimen, comprising receiving one or more digital images associated with a pathology specimen, identifying a plurality of pieces of tissue by applying an instance segmentation system to the one or more digital images, the instance segmentation system having been generated by processing a plurality of training images, determining, using the instance segmentation system, a prediction of whether any of the plurality of pieces of tissue were formerly conjoined, and outputting at least one instance segmentation to a digital storage device and/or display, the instance segmentation comprising an indication of whether any of the plurality of pieces of tissue were formerly conjoined.

Systems and methods to process electronic images to provide localized semantic analysis of whole slide images

Systems and methods are disclosed for providing automated routing of medical data, comprising determining at least one rule corresponding to at least one condition and at least one receiver, receiving medical data and associated medical metadata, determining whether the medical data, the associated medical metadata, and/or associated artificial intelligence processing satisfies the at least one condition of the at least one rule, and upon determining that the at least one condition of the at least one rule is satisfied, providing, from an originating institution, the medical data to the at least one receiver.

Systems and methods to process electronic images to provide automated routing of data

Systems and methods are disclosed for processing digital images to predict at least one continuous value comprising receiving one or more digital medical images, determining whether the one or more digital medical images includes at least one salient region, upon determining that the one or more digital medical images includes the at least one salient region, predicting, by a trained machine learning system, at least one continuous value corresponding to the at least one salient region, and outputting the at least one continuous value to an electronic storage device and/or display.

Systems and methods to process electronic images for continuous biomarker prediction

A method of using machine learning to output task-specific predictions may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.

Systems and methods for processing images to determine image-based computational biomarkers from liquid specimens

Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, detecting one or more image regions from a background of the one or more digital images, determining a prediction, using a machine learning system, of whether at least one first image region of the one or more image regions comprises at least one external contaminant, the machine learning system having been trained using a plurality of training images to predict a presence of external contaminants and/or a location of any external contaminants present in the tissue specimen, and determining, based on the prediction of whether a first image region comprises an external contaminant, whether to process the image region using an processing algorithm.

Systems and methods for processing electronic images to detect contamination in specimen preparations

A computer-implemented method may include receiving a collection of unstained digital histopathology slide images at a storage device and running a trained machine learning model on one or more slide images of the collection to infer a presence or an absence of a salient feature. The trained machine learning model may have been trained by processing a second collection of unstained or stained digital histopathology slide images and at least one synoptic annotation for one or more unstained or stained digital histopathology slide images of the second collection. The computer-implemented method may further include determining at least one map from output of the trained machine learning model and providing an output from the trained machine learning model to the storage device.

Systems and methods for processing electronic images to determine testing for unstained specimens

Systems and methods are disclosed for verifying slide and block quality for testing. The method may comprise receiving a collection of one or more digital images at a digital storage device. The collection may be associated with a tissue block and corresponding to an instance. The method may comprise applying a machine learning model to the collection to identify a presence or an absence of an attribute, determining an amount or a percentage of tissue with the attribute from a digital image in the collection that indicates the presence of the attribute, and outputting a quality score corresponding to the determined amount or percentage.

Systems and methods for artificial intelligence powered molecular workflow verifying slide and block quality for testing

Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.

Systems and methods for processing images to prepare slides for processed images for digital pathology

A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.

Systems and methods for processing electronic images of slides for a digital pathology workflow

A method of using a machine learning model to output a task-specific prediction may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.

Growing community of inventors

New York, NY, United States of America

Patricia Raciti