公开(公告)号：US20230186470A1

公开(公告)日：2023-06-15

申请号：US18064844

申请日：2022-12-12

Applicant: Ventana Medical Systems, Inc.

Inventor： Jungwon Kim ,  Auranuch Lorsakul ,  Yao Nie ,  Xingwei Wang ,  Zuo Zhao

IPC: G06T7/00 ,  G06V10/70 ,  G01N33/53

CPC classification number: G06T7/0012 ,  G06V10/70 ,  G01N33/53 ,  G06T2207/30024

Abstract: A multiplex image is accessed that depicts a particular slice of a particular sample stained with two or more dyes. Using a Generator network, a predicted singleplex image is generated that depicts the particular slice of the particular sample stained with each of the expressing biomarkers. The Generator network may have been trained by training a machine-learning model using a set of training multiplex images and a set of training singleplex images. Each of the set of training multiplex images depicted a slice of a sample stained with two or more dyes. Each of the set of training singleplex images depicted a slice of a sample stained with a single dye. The machine-learning model included a Discriminator network configured to discriminate whether a given image was generated by the Generator network or was a singleplex image of a real slide. The method further includes outputs the predicted singleplex image.

公开(公告)号：US20230230242A1

公开(公告)日：2023-07-20

申请号：US18170788

申请日：2023-02-17

Applicant: Ventana Medical Systems, Inc.

Inventor： Auranuch Lorsakul ,  Zuo Zhao ,  Yao Nie ,  Xingwei Wang ,  Kien Nguyen

IPC: G06T7/00

CPC classification number: G06T7/0012 ,  G06T2207/20084

Abstract: The present disclosure relates to techniques for transforming digital pathology images obtained by different slide scanners into a common format for image analysis. Particularly, aspects of the present disclosure are directed to obtaining a source image of a biological specimen, the source image is generated from a first type of scanner, inputting into a generator model a randomly generated noise vector and a latent feature vector from the source image as input data, generating, by the generator model, a new image based on the input data, inputting into a discriminator model the new image, generating, by the discriminator model, a probability for the new image being authentic or fake, determining whether the new image is authentic or fake based on the generated probability, and outputting the new image when the image is authentic.