Tempus Labs, Inc.

Tempus Ai, Inc.

Wisconsin Alumni Research Foundation

Aiq Global, Inc.

Stephen Yip

Lingdao Sha

Boleslaw Osinski

Irvin Ho

Aly Azeem Khan

Andrew J Kruger

Michael Carlson

Abel Greenwald

Caleb Willis

Ryan Jones

Andrew Westley

Brett Mahon

Robert Jeraj

Timothy Perk

Glenn Liu

Daniel Huff

A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Determining biomarkers from histopathology slide images

A platform is provided for generating 3D models of a tumor segmented from a series of 2D medical images and for identifying from these 3D models, radiomic features that may be used for diagnostic, prognostic, and treatment response assessment of the tumor. The radiomic features may be shape-based features, intensity-based features, textural features, and filter-based features. The radiomic features are compared to remove sufficiently redundant features, thereby producing a reduced set of radiomic features, which is then compared to separate genomic data and/or outcome data to identify clinically and biologically significant radiomic features for diagnostic, prognostic, and treatment response assessment, other applications.

3D radiomic platform for imaging biomarker development

A method for qualifying a specimen prepared on one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and providing associated unstained slides for subsequent nucleic acid analysis is provided.

Predicting total nucleic acid yield and dissection boundaries for histology slides

A system for identifying biomarkers in a digital image of a Hematoxylin and Eosin-stained slide of a target tissue includes a processor and an electronic network; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: process segmented tile images determine a predicted biomarker presence; and transmit the predicted presence. A non-transitory computer-readable medium includes a set of computer-executable instructions that, when executed by one or more processors, cause a computer to: process segmented tile images; determine a predicted biomarker presence; and transmit the predicted presence. A computer-implemented method includes processing segmented tile images; determining a predicted biomarker presence; and transmitting the predicted presence.

A system for monitoring organ health during treatment for cancer and the like makes use of physiological imaging of the kind used for treatment monitoring and organ-specific processing to provide a comprehensive assessment of treatment side-effects.

Apparatus for monitoring treatment side effects

A method for determining tumor block sufficiency for generating one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and determining a number of H&E slide for satisfying a desired total nucleic yield is provided.

A method for predicting an expected yield of nucleic acid from tumor cells within a dissection boundary on a hematoxylin and eosin (H&E) slide is provided.

Techniques for generating an overlay map on a digital medical image of a slide are provided, and include cell detection and tissue classification processes. Techniques include receiving a medical image, separating the image into tiles, and performing tile classifications and tissue classifications based on a multi-tile analysis. Techniques additionally include identifying cell objects in the image, separating the image into and displaying polygons identifying the cell objects and cell classifications. Generated displays may be overlays over the initial digital image.

Growing community of inventors

Chicago, IL, United States of America

Stephen Yip