公开(公告)号：US11587228B2

公开(公告)日：2023-02-21

申请号：US16989968

申请日：2020-08-11

Applicant: Nano-X AI Ltd.

Inventor： Amir Bar ,  Raouf Muhamedrahimov ,  Rachel Wities

IPC: G06K9/00 ,  G06T7/00 ,  G06F40/20 ,  G16H30/40 ,  G06N20/00 ,  G16H15/00

Abstract: There is provided a method, comprising: providing a training dataset including, medical images and corresponding text based reports, and concurrently training a natural language processing (NLP) machine learning (ML) model for generating a NLP category for a target text based report and a visual ML model for generating a visual finding for a target image, by: training the NLP ML model using the text based reports of the training dataset and a ground truth comprising the visual finding generated by the visual ML model in response to an input of the images corresponding to the text based reports of the training dataset, and training the visual ML model using the images of the training dataset and a ground truth comprising the NLP category generated by the NLP ML model in response to an input of the text based reports corresponding to the images of the training dataset.

公开(公告)号：US11727087B2

公开(公告)日：2023-08-15

申请号：US17221860

申请日：2021-04-05

Applicant: Nano-X AI Ltd.

Inventor： Raouf Muhamedrahimov ,  Amir Bar

IPC: G06T7/00 ,  G06F18/214 ,  G06N3/08 ,  A61B6/00 ,  G06F18/23 ,  G06F18/213 ,  G06N3/045

CPC classification number: G06F18/2148 ,  A61B6/481 ,  A61B6/484 ,  G06F18/213 ,  G06F18/2155 ,  G06F18/23 ,  G06N3/045 ,  G06N3/08 ,  G06T7/0012 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06V2201/03

Abstract: There is provided a method, comprising: accessing medical images of subjects, depicting contrast phases of contrast administered to the respective subject, accessing for a first subset of the medical images, metadata indicating a respective contrast phase, wherein a second subset of the medical images are unassociated with metadata, mapping each respective contrast phase of the contrast phases to a respective time interval indicating estimated amount of time from a start of contrast administration to time of capture of the respective medical image, creating a training dataset, by labelling images of the first subset with a label indicating the respective time interval, and including the second subset as non-labelled images, and training the ML model using the training dataset for generating an outcome of a target time interval indicating estimated amount of time from the start of contrast administration, in response to an input of a target medical image.