Computerized systems and methods for detecting features in electronic images
AssigneeSingular Genomics Systems, Inc.
InventorsWalter Pupin FU, Michael OUIMET, Andrew WALTERS
Life of this patent
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Abstract
Disclosed herein, inter alia, are systems and methods for extracting features from sequencing images using machine learning, involving the construction of a kernel bank to handle variations in imaging conditions, compressing extracted vector intensities into super intensity vectors, and improving basecalling accuracy and robustness to misfocus and aberrations.
Claims18
A computer-implemented method for extracting features of sequencing images, comprising:
- extracting vector intensities, using at least a machine learning model trained on a set of sequencing images generated from a plurality of imaging systems, of an image patch of the sequencing images;
- and
- compressing, using at least the machine learning model, the vector intensities into a plurality of super intensity vectors.
A computer-implemented method for extracting features of an image, comprising:
- constructing a kernel bank comprising learned linear combinations of a set of precomputed two-dimensional basis vectors trained simultaneously with a machine learning nonlinear extraction network that is trained on a set of sequencing images generated from a plurality of imaging systems;
- and
- pre-computing, using at least the machine learning nonlinear extraction network, a set of optimal linear combinations.
A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform a method for analyzing a tissue sample, the method comprising:
- extracting vector intensities, using at least a machine learning model trained on a set of sequencing images generated from a plurality of imaging systems, of an image patch of the sequencing images;
- and
- compressing, using at least the machine learning model, the vector intensities into a plurality of super intensity vectors.
A system for analyzing a tissue sample, the system comprising:
- (a) a memory storing instructions;
- (b) a processor configured to execute the instructions to:
- extracting vector intensities, using at least a machine learning model trained on a set of sequencing images generated from a plurality of imaging systems, of an image patch of the sequencing images;
- and
- compressing, using at least the machine learning model, the vector intensities into a plurality of super intensity vectors.
Dependencies are inferred from the claim text.