公开(公告)号：US20240203101A1

公开(公告)日：2024-06-20

申请号：US18536174

申请日：2023-12-11

Applicant: Genentech, Inc.

Inventor： Miao ZHANG ,  Nagamurali K. MOVVA ,  Mahdi ABBASPOUR TEHRANI

IPC: G06V10/774 ,  G06T7/00 ,  G06V10/82

CPC classification number: G06V10/774 ,  G06T7/0012 ,  G06V10/82 ,  G06T2207/20104 ,  G06T2207/30041 ,  G06V2201/03

Abstract: Systems and methods for annotating medical images using an artificial intelligence (AI)-enabled workflow are disclosed herein. In some example embodiments, an image of a sample may be labeled using an annotation generated by a neural network The annotation may represent a feature in the image. In some instances, the labeled image may be reviewed by laypersons and/or experts for accuracy and/or completeness, and the labeled image may be updated based on the review to generate an annotated image.

公开(公告)号：US20250061574A1

公开(公告)日：2025-02-20

申请号：US18939497

申请日：2024-11-06

Applicant: Genentech, Inc.

Inventor： Heming YAO ,  Miao ZHANG ,  Seyed Mohammadmohsen HEJRATI

IPC: G06T7/00 ,  A61B3/00 ,  A61B3/10 ,  A61B3/12 ,  G06T7/73 ,  G16H50/20

Abstract: A method and system for detecting nascent geographic atrophy. An optical coherence tomography (OCT) volume image of a retina of a subject is received. Using a deep learning model, an output is generated using the OCT volume image in which the output indicates whether nascent geographic atrophy is detected. A map output is generated for the deep learning model using a saliency mapping algorithm, wherein the map output indicates a level of contribution of a set of regions in the OCT volume image to the output generated by the deep learning model.

公开(公告)号：US20240293024A1

公开(公告)日：2024-09-05

申请号：US18646646

申请日：2024-04-25

Applicant: Genentech, Inc.

Inventor： Seyed Mohammadmohsen HEJRATI ,  Heming YAO ,  Miao ZHANG

IPC: A61B3/12 ,  A61B3/00 ,  A61B3/10 ,  A61B5/00 ,  G16H50/30

CPC classification number: A61B3/1225 ,  A61B3/0025 ,  A61B3/102 ,  A61B5/7267 ,  G16H50/30 ,  A61B2576/02

Abstract: Systems and methods for determining the health status of a retina of a subject. An optical coherence tomography (OCT) volume image of a retina of a subject may be received. A health indication output is generated, via a deep learning model, using the OCT volume image. The health indication output indicates a level of association between the OCT volume image and a selected health status category for the retina. A map output for the deep learning model is generated using a saliency mapping algorithm, generating a map output for the deep learning model using a saliency mapping algorithm. The map output indicates a level of contribution of a set of regions in the OCT volume image to the health indication output generated by the deep learning model.