View source for US Patent Application 17804224. GENERATING NEURAL NETWORKS TAILORED TO OPTIMIZE SPECIFIC MEDICAL IMAGE PROPERTIES USING NOVEL LOSS FUNCTIONS simplified abstract

=GENERATING NEURAL NETWORKS TAILORED TO OPTIMIZE SPECIFIC MEDICAL IMAGE PROPERTIES USING NOVEL LOSS FUNCTIONS=


==Organization Name==

[[:Category:GE Precision Healthcare LLC|GE Precision Healthcare LLC]]


[[Category:GE Precision Healthcare LLC]]

==Inventor(s)==

[[:Category:Obaidullah Rahman of South Bend IN (US)|Obaidullah Rahman of South Bend IN (US)]][[Category:Obaidullah Rahman of South Bend IN (US)]]

[[:Category:Madhuri Mahendra Nagare of Karmala IN (US)|Madhuri Mahendra Nagare of Karmala IN (US)]][[Category:Madhuri Mahendra Nagare of Karmala IN (US)]]

[[:Category:Roman Melnyk of New Berlin WI (US)|Roman Melnyk of New Berlin WI (US)]][[Category:Roman Melnyk of New Berlin WI (US)]]

[[:Category:Jie Tang of Merion Station PA (US)|Jie Tang of Merion Station PA (US)]][[Category:Jie Tang of Merion Station PA (US)]]

[[:Category:Brian E. Nett of Wauwatosa WI (US)|Brian E. Nett of Wauwatosa WI (US)]][[Category:Brian E. Nett of Wauwatosa WI (US)]]

[[:Category:Charles Addison Bouman of West Lafayette IN (US)|Charles Addison Bouman of West Lafayette IN (US)]][[Category:Charles Addison Bouman of West Lafayette IN (US)]]

[[:Category:Ken Sauer of South Bend IN (US)|Ken Sauer of South Bend IN (US)]][[Category:Ken Sauer of South Bend IN (US)]]

==GENERATING NEURAL NETWORKS TAILORED TO OPTIMIZE SPECIFIC MEDICAL IMAGE PROPERTIES USING NOVEL LOSS FUNCTIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17804224 titled 'GENERATING NEURAL NETWORKS TAILORED TO OPTIMIZE SPECIFIC MEDICAL IMAGE PROPERTIES USING NOVEL LOSS FUNCTIONS

==Simplified Explanation==

- This patent application describes techniques for generating neural networks (NNs) that are specifically designed to optimize certain properties of medical images.
- The system includes a memory and a processor, which work together to execute computer executable components.
- One of these components is a training component, which trains a NN to generate a modified version of computed tomography (CT) data with optimized properties.
- The training process uses a novel loss function that is tailored to control the learning adaptation of the NN based on the error associated with specific components of the CT data.
- These defined components can include frequency components or spatial feature components.
- The result of the training process is a trained NN that can generate modified CT data with improved properties.


==Original Abstract Submitted==

Techniques are described that facilitate generating neural network (NNs) tailored to optimize specific properties of medical images using novel loss functions. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a training component that trains a NN to generate a modified version of computed tomography (CT) data comprising one or more optimized properties relative to the CT data using a loss function tailored to control learning adaptation of the NN based on error attributed to one or more defined components associated with the CT data, resulting in a trained NN, wherein the one or more defined components comprise at least one of a frequency component or a spatial feature component.



[[Category:G06N3/08]]
[[Category:G16H30/40]]