Invention Grant
- Patent Title: Deep learning based instance segmentation via multiple regression layers
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Application No.: US16846180Application Date: 2020-04-10
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Publication No.: US11410303B2Publication Date: 2022-08-09
- Inventor: Elad Arbel , Itay Remer , Amir Ben-Dor
- Applicant: Agilent Technologies, Inc.
- Applicant Address: US CA Santa Clara
- Assignee: Agilent Technologies, Inc.
- Current Assignee: Agilent Technologies, Inc.
- Current Assignee Address: US CA Santa Clara
- Agency: Adsero IP
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06T7/00 ; G06T7/10 ; G06T7/187 ; G06T7/11 ; G06T7/174 ; G06N20/00 ; G06F3/0482 ; G06F3/0486 ; G06K9/62 ; G06N3/08 ; G06V10/75

Abstract:
Novel tools and techniques are provided for implementing digital microscopy imaging using deep learning-based segmentation and/or implementing instance segmentation based on partial annotations. In various embodiments, a computing system might receive first and second images, the first image comprising a field of view of a biological sample, while the second image comprises labeling of objects of interest in the biological sample. The computing system might encode, using an encoder, the second image to generate third and fourth encoded images (different from each other) that comprise proximity scores or maps. The computing system might train an AI system to predict objects of interest based at least in part on the third and fourth encoded images. The computing system might generate (using regression) and decode (using a decoder) two or more images based on a new image of a biological sample to predict labeling of objects in the new image.
Public/Granted literature
- US20200327667A1 Deep Learning Based Training of Instance Segmentation via Regression Layers Public/Granted day:2020-10-15
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