Invention Grant
- Patent Title: Uncertainty-refined image segmentation under domain shift
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Application No.: US16887311Application Date: 2020-05-29
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Publication No.: US11379991B2Publication Date: 2022-07-05
- Inventor: Carianne Martinez , Kevin Matthew Potter , Emily Donahue , Matthew David Smith , Charles J. Snider , John P. Korbin , Scott Alan Roberts , Lincoln Collins
- Applicant: National Technology & Engineering Solutions of Sandia, LLC
- Applicant Address: US NM Albuquerque
- Assignee: National Technology & Engineering Solutions of Sandia, LLC
- Current Assignee: National Technology & Engineering Solutions of Sandia, LLC
- Current Assignee Address: US NM Albuquerque
- Agency: Yee & Associates, P.C.
- Main IPC: G06T7/174
- IPC: G06T7/174 ; G06N3/08 ; G06N5/04 ; G06K9/62 ; G06F17/18

Abstract:
A method for digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training. The neural network receives image data from a second, different domain. A vector of N values that sum to 1 is calculated for each image element, wherein each value represents an image segmentation class. A label is assigned to each image element according to the class with the highest value in the vector. Multiple inferences are performed with active dropout layers for each image element, and an uncertainty value is generated for each image element. The label of any image element with an uncertainty value above a predefined threshold is replaced with a new label corresponding to the class with the next highest value.
Public/Granted literature
- US20210374968A1 UNCERTAINTY-REFINED IMAGE SEGMENTATION UNDER DOMAIN SHIFT Public/Granted day:2021-12-02
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