The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 09, 2023

Filed:

Jun. 09, 2021
Applicant:

Annalise-ai Pty Ltd, Sydney, AU;

Inventors:

Dang-Dinh-Ang Tran, Sydney, AU;

Jarrel Seah, Sydney, AU;

David Huang, Sydney, AU;

David Vuong, Sydney, AU;

Xavier Holt, Sydney, AU;

Marc Justin Nothrop, Sydney, AU;

Benjamin Austin, Sydney, AU;

Aaron Lee, Sydney, AU;

Marco Amoroso, Sydney, AU;

Assignee:

Annalise AI Pty Ltd, Sydney, AU;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 50/20 (2018.01); G16H 30/40 (2018.01); A61B 6/00 (2006.01); G06V 10/82 (2022.01); G16H 40/67 (2018.01);
U.S. Cl.
CPC ...
G16H 50/20 (2018.01); A61B 6/5217 (2013.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 40/67 (2018.01);
Abstract

This disclosure relates to detecting visual findings in anatomical images. Methods comprise inputting anatomical images into a neural network to output a feature vector and computing an indication of visual findings being present in the images by a dense layer of the neural network that takes as input the feature vector and outputs an indication of whether each of the visual findings is present in the anatomical images. The neural network is trained on a training dataset including anatomical images, and labels associated with the anatomical images and each of the visual findings. The visual findings may be organised as a hierarchical ontology tree. The neural network may be trained by evaluating the performance of neural networks in detecting the visual findings and a negation pair class which comprises anatomical images where a first visual finding is identified in the absence of a second visual finding.


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