公开(公告)号：US20230135258A1

公开(公告)日：2023-05-04

申请号：US17914737

申请日：2021-03-23

Applicant: Genentech, Inc.

Inventor： Jasmine Patil ,  Neha Sutheekshna Anegondi ,  Alexandre J Fernandez Coimbra ,  Simon Shang Gao ,  Michael Gregg Kawczynski

IPC: G06T7/00 ,  G06T7/11 ,  G06T7/60 ,  G06V10/82

Abstract: A method, system, and computer program product for evaluating a geographic atrophy lesion. An image of a geographic atrophy (GA) lesion is received. A first set of values is determined for a set of shape features using the image. A second set of values is determined for a set of textural features using the image. GA progression for the GA lesion is predicted using the first set of values and the second set of values.

公开(公告)号：US12271816B2

公开(公告)日：2025-04-08

申请号：US17885221

申请日：2022-08-10

Applicant: Genentech, Inc.

Inventor： Jasmine Patil

IPC: G06N3/08 ,  G06V10/764 ,  G06V10/82

Abstract: A data set can be provided that includes an input data element and one or more label data portion definitions that each identify a feature of interest within the input data element. A machine-learning model can generate model-identified portions definitions that identify predicted feature of interests within the input data element. At least one false negative (where a feature of interest is identified without a corresponding predicted feature of interest) and at least one false positive (where a predicted feature of interest is identified without a corresponding feature of interest) can be a identified. A class-disparate loss function can be provided that is configured to penalize false negatives more than at least some false positives. A loss can be calculated using the class-disparate loss function. A set of parameter values of the machine-learning model can be determined based on the loss.