公开(公告)号：US20240104948A1

公开(公告)日：2024-03-28

申请号：US18516417

申请日：2023-11-21

Applicant: Genentech, Inc.

Inventor： Jeffrey Ryan EASTHAM ,  Hartmut Koeppen ,  Xiao Li ,  Darya Yuryevna Orlova

IPC: G06V20/69 ,  G06T7/00 ,  G06T7/11 ,  G06V10/25 ,  G06V10/26 ,  G06V10/77 ,  G06V10/82

CPC classification number: G06V20/698 ,  G06T7/0012 ,  G06T7/11 ,  G06V10/25 ,  G06V10/267 ,  G06V10/77 ,  G06V10/82 ,  G06V20/695 ,  G06T2207/10056 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Systems and methods relate to processing digital pathology images. More specifically, techniques include accessing a digital pathology image that depicts a section of a biological sample, wherein the digital pathology image comprises regions displaying reactivity to a plurality of stains. For each of a plurality of tiles of the digital pathology image, a local-density measurement is calculated for each of a plurality of biological object types. One or more spatial-distribution metrics may be generated for the biological object types based at least in part on the calculated local-density measurements. A tumor immunophenotype may then be generated for the digital pathology image based at least in part on the local-density measurements or the one or more spatial-distribution metrics.

公开(公告)号：US20240038393A1

公开(公告)日：2024-02-01

申请号：US18449632

申请日：2023-08-14

Applicant: GENENTECH, INC. ,  HOFFMANN-LA ROCHE INC.

Inventor： Xiao Li ,  Marius Rene Garmhausen ,  Gunther Jansen ,  Teresa Melanie Karrer

IPC: G16H50/20 ,  G16H50/30 ,  G16H30/40 ,  G16B25/10 ,  G16B40/20

CPC classification number: G16H50/20 ,  G16H50/30 ,  G16H30/40 ,  G16B25/10 ,  G16B40/20

Abstract: In some embodiments, a current state of a medical condition or a progression of the medical condition is predicted by processing one or more digital pathology images and expression levels of genes using a machine-learning model. In some embodiments, one or more predicted gene-expression levels are generated by processing a data set corresponding to one or more digital pathology images using a machine-learning model. In some embodiments, one or more predicted digital pathology metrics are generated by processing a data set that corresponds to expression levels of a set of genes using a machine-learning model.