公开(公告)号：US20240185577A1

公开(公告)日：2024-06-06

申请号：US18285361

申请日：2022-03-24

Applicant: Bayer Aktiengesellschaft

Inventor： Matthias LENGA ,  Marvin PURTORAB ,  Thiago RAMOS DOS SANTOS ,  Jens HOOGE ,  Veronica CORONA

IPC: G06V10/774 ,  G06V10/22 ,  G06V10/32 ,  G06V10/82

CPC classification number: G06V10/774 ,  G06V10/22 ,  G06V10/32 ,  G06V10/82 ,  G06V2201/031

Abstract: The present invention relates to the technical field of machine learning. Subject matter of the present invention is a novel approach for training a neural network and the use of this approach for the processing of (medical) images.

公开(公告)号：US20240303973A1

公开(公告)日：2024-09-12

申请号：US18547855

申请日：2022-02-16

Applicant: Bayer Aktiengesellschaft

Inventor： Thiago RAMOS DOS SANTOS ,  Veronica CORONA ,  Marvin PURTORAB ,  Sara LORIO

IPC: G06V10/774 ,  G06T11/00 ,  G06V10/46 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06V10/774 ,  G06T11/00 ,  G06V10/462 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06T2210/41 ,  G06V2201/03

Abstract: The present invention provides a technique for model improvement in supervised learning with potential applications to a variety of imaging tasks, such as segmentation, registration, detection. In particular, it has shown potential in medical imaging enhancement.

公开(公告)号：US20240193738A1

公开(公告)日：2024-06-13

申请号：US18556528

申请日：2022-04-13

Applicant: Bayer Aktiengesellschaft

Inventor： Veronica CORONA ,  Marvin PURTORAB ,  Sara LORIO ,  Thiago RAMOS DOS SANTOS

IPC: G06T5/50 ,  G06T5/60

CPC classification number: G06T5/50 ,  G06T5/60 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A method of training a prediction tool to generate at least one synthetic full-contrast image from zero-contrast and low-contrast images of a subject may involve receiving a training set a set of images of a set of subjects, the images of each subject comprising a full-contrast image, a low-contrast image, a first zero-contrast image acquired prior to the acquisition of the full-contrast image, and a second zero-contrast image acquired prior to the acquisition of the low-contrast image. An artificial neural network may be trained with the training set by applying the first and second zero-contrast images from the set of images and the low-contrast images from the set of images as input to the artificial neural network and using a cost function to compare the output of the artificial neural network with the full-contrast images from the set of images to train parameters of the artificial neural network using backpropagation.