Invention Grant
- Patent Title: Deep learning in label-free cell classification and machine vision extraction of particles
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Application No.: US15928992Application Date: 2018-03-22
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Publication No.: US10593039B2Publication Date: 2020-03-17
- Inventor: Bahram Jalali , Ata Mahjoubfar , Lifan Chen
- Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- Applicant Address: US CA Oakland
- Assignee: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- Current Assignee: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- Current Assignee Address: US CA Oakland
- Agency: O'Banion & Ritchey LLP
- Agent John P. O'Banion
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06T7/00 ; G16B40/00 ; G06N3/08 ; G01N15/14 ; G06K9/46 ; G06N3/04 ; G01N15/10

Abstract:
A method and apparatus for using deep learning in label-free cell classification and machine vision extraction of particles. A time stretch quantitative phase imaging (TS-QPI) system is described which provides high-throughput quantitative imaging, and utilizing photonic time stretching. In at least one embodiment, TS-QPI is integrated with deep learning to achieve record high accuracies in label-free cell classification. The system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. The system is particularly well suited for data-driven phenotypic diagnosis and improved understanding of heterogeneous gene expression in cells.
Public/Granted literature
- US20180286038A1 DEEP LEARNING IN LABEL-FREE CELL CLASSIFICATION AND MACHINE VISION EXTRACTION OF PARTICLES Public/Granted day:2018-10-04
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