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:
Apr. 08, 2025

Filed:

Mar. 14, 2023
Applicant:

The General Hospital Corporation, Boston, MA (US);

Inventors:

Matthew S. Rosen, Somerville, MA (US);

Bo Zhu, Cambridge, MA (US);

Bruce R. Rosen, Lexington, MA (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); A61B 5/00 (2006.01); A61B 5/055 (2006.01); A61B 6/00 (2024.01); A61B 6/03 (2006.01); A61B 6/46 (2024.01); A61B 8/00 (2006.01); A61B 8/08 (2006.01); G01R 33/12 (2006.01); G01R 33/48 (2006.01); G01R 33/56 (2006.01); G06F 18/2113 (2023.01); G06F 18/2413 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06T 11/00 (2006.01); G06V 10/764 (2022.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); A61B 5/0035 (2013.01); A61B 5/0059 (2013.01); A61B 5/055 (2013.01); A61B 5/7267 (2013.01); A61B 5/7425 (2013.01); A61B 6/032 (2013.01); A61B 6/037 (2013.01); A61B 6/463 (2013.01); A61B 6/5247 (2013.01); A61B 8/463 (2013.01); A61B 8/5261 (2013.01); G01R 33/12 (2013.01); G01R 33/4818 (2013.01); G01R 33/5608 (2013.01); G06F 18/2113 (2023.01); G06F 18/24143 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 11/006 (2013.01); G06V 10/764 (2022.01); G16H 30/40 (2018.01); G01R 33/4824 (2013.01); G06T 2207/20024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2210/41 (2013.01); G06V 2201/03 (2022.01);
Abstract

A system may transform sensor data from a sensor domain to an image domain using data-driven manifold learning techniques which may, for example, be implemented using neural networks. The sensor data may be generated by an image sensor, which may be part of an imaging system. Fully connected layers of a neural network in the system may be applied to the sensor data to apply an activation function to the sensor data. The activation function may be a hyperbolic tangent activation function. Convolutional layers may then be applied that convolve the output of the fully connected layers for high level feature extraction. An output layer may be applied to the output of the convolutional layers to deconvolve the output and produce image data in the image domain.


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