Invention Grant
- Patent Title: Neural network-based object detection in visual input
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Application No.: US16989625Application Date: 2020-08-10
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Publication No.: US11043297B2Publication Date: 2021-06-22
- Inventor: Christine I. Podilchuk , Richard Mammone
- Applicant: Rutgers, The State University of New Jersey
- Applicant Address: US NJ New Brunswick
- Assignee: Rutgers, The State University of New Jersey
- Current Assignee: Rutgers, The State University of New Jersey
- Current Assignee Address: US NJ New Brunswick
- Agency: Greenberg Traurig, LLP
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G16H30/40 ; A61B5/00 ; A61B8/08 ; G06T7/00 ; G16H30/20 ; G16H50/20 ; G06K9/32 ; G06N3/08

Abstract:
A device to detect an object in a medical image is described. An image analysis application, executed by the device, receives the medical image as an input. The medical image is next partitioned to sub-regions. Parts of the object are detected in a selection of the sub-regions using a deep-learning neural network (DNN) model. Bounding boxes for the selection are also determined. The bounding boxes are evaluated based on a confidence score detected as above a threshold level. The confidence score designates the parts as contained within the selection. Next, a region of interest (ROI) is determined as a group including the selection. Similar orientations associated with the bounding boxes are comparable to similar orientations of a positive training model of the DNN model. Furthermore, the selection is designated as the ROI within the medical image. The medical image is provided with the ROI to a user.
Public/Granted literature
- US20210050095A1 NEURAL NETWORK-BASED OBJECT DETECTION IN VISUAL INPUT Public/Granted day:2021-02-18
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