Paige.ai, Inc.

Memorial Sloan Kettering Cancer Center

Peter Schueffler

Razik Yousfi

Thomas Fuchs

Thomas Fresneau

Jillian Sue

Leo John Grady

Alexander Tsema

Jason Locke

Alexandre Kirszenberg

Christopher Kanan

Matthew Hanna

Dig Vijay Kumar Yarlagadda

Jose Sebastian Izurieta-Herrera

The present disclosure relates generally to image viewers, in particular biomedical images viewers that can concurrently render biomedical images at various magnifications or resolutions. The computing system can identify tiles from a first portion of a biomedical image. Each tile can correspond to a magnification level and coordinates in the biomedical image. The computing system can provide the tiles for concurrent display in respective graphical user interface (GUI) elements. The computing system can detect an interaction with a GUI element. The computing system can identify a change in coordinates for a tile displayed in the GUI element based on the interaction. The computing system can determine a second change in the other concurrently displayed tiles based on the change in coordinates. The computing system can update the concurrent display of the tiles based on the changes.

Universal slide viewer

Systems and methods are disclosed for using an integrated computing platform to view and transfer digital pathology slides using artificial intelligence, the method including receiving at least one whole slide image in a cloud computing environment located in a first geographic region, the whole slide image depicting a medical sample associated with a patient, the patient being located in the first geographic region; storing the received whole slide image in a first encrypted bucket; applying artificial intelligence to perform a classification of the at least one whole slide image, the classification comprising steps to determine whether portions of the medical sample depicted in the whole slide image are healthy or diseased; based on the classification of the at least one whole slide image, generating metadata associated with the whole slide image; and storing the metadata in a second encrypted bucket.

Systems and methods of automatically processing electronic images across regions

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.

Systems and methods for analyzing electronic images for quality control

Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target electronic image, the machine learning system having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the target electronic image identifying an area of interest based on the at least one characteristic of the target specimen and/or the at least one characteristic of the target electronic image.

Systems and methods for processing images of slides for digital pathology

Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, a related case, a patient, and/or a plurality of clinical information, determining one or more of a prediction, a recommendation, and/or a plurality of data for the one or more digital images using a machine learning system, the machine learning system having been trained using a plurality of training images, to predict a biomarker and a plurality of genomic panel elements, and determining, based on the prediction, the recommendation, and/or the plurality of data, whether to log an output and at least one visualization region as part of a case history within a clinical reporting system.

Systems and methods for delivery of digital biomarkers and genomic panels

Described herein are systems and methods of determining primary sites from biomedical images. A computing system may identify a first biomedical image of a first sample from one of a primary site or a secondary site associated with a condition in a first subject. The computing system may apply the first biomedical image to a site prediction model comprising a plurality of weights to determine the primary site for the condition. The computing system may store an association between the first biomedical image and the primary site determined using the site prediction model.

Machine learning model for analyzing pathology data from metastatic sites

A method for processing an electronic image including receiving, by a viewer, the electronic image and a FOV (field of view), wherein the FOV includes at least one coordinate, at least one dimension, and a magnification factor, loading, by the viewer, a plurality of tiles within the FOV, determining, by the viewer, a state of the plurality of tiles in a cache, and in response to determining that the state of the plurality of tiles in the cache is a fully loaded state, rendering, by the viewer, the plurality of tiles to a display.

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New York, NY, United States of America

Peter Schueffler