Back to results
US 2022/0284697 A1Published application
Training data generation with generative modeling
AssigneeCalifornia Institute of Technology
InventorsEmily C. Laubscher, Nitzan Razin, David A. Van Valen
Life of this patent
Estimating the term…
Abstract
Disclosed herein include systems, devices, and methods for generating training data for spot detection using generative models with expectation maximization. The training data can be used to train a machine learning model for spot detection.
Claims27
A method for generating training data for spot detection comprising:
- under control of a hardware processor:
- receiving a training image;
- for each of a plurality of spot detection methods, detecting a plurality of spots on the training image using the spot detection method;
- clustering spots of pluralities of spots to generate a plurality of clusters each comprising one or more spots of the pluralities of spots;
- iteratively determining a true positive rate (TPR) and a false positive rate (FPR) for each of the plurality of spot detection methods until the TPR of the spot detection method converge and the FPR of the spot detection method converge, comprising, in each iteration:
- for each of the plurality of clusters, determining a probability of the cluster being a true positive (TP) and a probability of the cluster being a false positive (FP) using a TPR and a FPR of the spot detection method, wherein the TPR and the FPR are an initial true positive rate (TPR) and an initial false positive rate (FPR), respectively, or a TPR and a FPR determined from a prior iteration, respectively;
- determining (i) an expectation value of the cluster being a TP, (ii) an expectation value of the cluster being a false negative (FN), (iii) an expectation value of the cluster being a FP, and (iv) an expectation value of the cluster being a true negative (TN) using the probability of the cluster being a TP and the probability of the cluster being a FP and whether the cluster comprises a spot of the plurality of spots determined using the spot detection method;
- determining (i) an expectation value of a total number of TPs, (ii) an expectation value of a total number of FNs, (iii) an expectation value of a total number of FPs, and (iv) an expectation value of a total number of TNs using the expectation values of each of the plurality of clusters being a TP, a FN, a FP, and a TN;
- and
- determining a TPR and a FPR for spot detection method for the iteration using the expectation values of the total numbers of TPs, FNs, FPs, and TNs;
- and
- generating training data comprising the training image and a plurality of training clusters comprising clusters of the plurality of clusters.
9 more elements
(canceled)
A method for generating training data for spot detection comprising:
- under control of a hardware processor:
- receiving a plurality of training images;
- for each training image:
- for each of a plurality of spot detection methods, detecting a plurality of spots on the training image using the spot detection method;
- clustering spots of pluralities of spots to generate a plurality of clusters each comprising one or more spots of the pluralities of spots;
- performing generative modeling with expectation maximization to determine a true positive rate (TPR) and a false positive rate (FPR) for each of the plurality of spot detection methods;
- and
- generating training data comprising the plurality of training images and a plurality of training clusters comprising clusters of the plurality of clusters for each of the plurality of images.
5 more elements
-7. (canceled)
(canceled)
-24. (canceled)
-30. (canceled)
A system for spot detection comprising:
- non-transitory memory configured to store:
- executable instructions, and
- a machine learning model, wherein the machine learning model is trained using training data comprising a plurality of training images and a plurality of training clusters on each of the plurality of training images, wherein the plurality of training clusters is generated based on (a) a plurality of clusters each comprising one or more spots of pluralities of spots on a training image, each plurality of spots detected on the training image using one of a plurality of spot detection methods and (b) generative modeling with expectation maximization on the plurality of clusters which determines a true positive rate (TPR) and a false positive rate (FPR) for each of the plurality of spot detection methods for the training image;
- and
- a hardware processor in communication with the non-transitory memory, the hardware processor programmed by the executable instructions to perform:
- receiving an input image;
- performing spot detection on the input image using the machine learning model.
4 more elements
(canceled)
-55. (canceled)
Dependencies are inferred from the claim text.