Systems and methods for training generative adversarial networks and use of trained generative adversarial networks
The present disclosure relates to computer-implemented systems and methods for training and using generative adversarial networks. In one implementation, a system for training a generative adversarial network may include at least one processor that may provide a first plurality of images including representations of a feature-of-interest and indicators of locations of the feature-of-interest and use the first plurality and indicators to train an object detection network. Further, the processor(s) may provide a second plurality of images including representation of the feature-of-interest, and apply the trained object detection network to the second plurality to produce a plurality of detections of the feature-of-interest. Additionally, the processor(s) may provide manually set verifications of true positives and false positives with respect to the plurality of detections, use the verifications tr train a generative adversarial network, and retrain the generative adversarial network using at least one further set of images, further detections, and further manually set verifications.
1. A method for training a generative adversarial network, comprising:
receiving a first plurality of feature indicators for at least one region in a first plurality of image frames, the at least one region including one or more representations of a feature-of-interest;
training a discriminator network using a first training set including the first plurality of image frames and the first plurality of feature indicators;
applying the trained discriminator network to a second plurality of image frames to produce a second plurality of feature indicators for at least one region in the second plurality of image frames, the at least one region including one or more representations of the feature-of-interest;
receiving verifications of true positives and false positives with respect to the second plurality of feature indicators; and
training a generative adversarial network using a second training set including the second plurality of image frames and the verifications.
2. The method of claim 1 , further comprising extracting the first plurality of image frames from a database.
3. The method of claim 1 , further comprising displaying, via a display device, the first plurality of image frames.
4. The method of claim 1 , wherein the first plurality of feature indicators are received from an input device.
5. The method of claim 1 , further comprising extracting the second plurality of image frames from a database.
6. The method of claim 1 , wherein the generative adversarial network includes a generative network and an adversarial network.
7. The method of claim 6 , wherein training the generative adversarial network includes training the generative network to produce artificial representations of the feature-of-interest.
8. The method of claim 7 , wherein training the generative adversarial network further includes training the adversarial network to distinguish the artificial representations of the feature-of-interest from real representations of the feature-of-interest.
9. The method of claim 6 , wherein training the generative adversarial network further comprises training the generative network to produce artificial representations of a false feature-of-interest that looks similar to a true feature-of-interest.
10. The method of claim 1 , wherein the first plurality of image frames and the second plurality of image frames comprise medical images captured by an imaging device.
11. The method of claim 1 , wherein the feature-of-interest includes an abnormality comprising at least one of a formation on or of human tissues, a change in human tissue from one type of cell to another type of cell, an absence of human tissue from a location where the human tissue is expected, and a lesion.
12. The method of claim 1 , wherein the discriminator network comprises an object detection network.
13. The method of claim 1 , wherein training the discriminator network includes adjusting one or more weights associated with one or more nodes of the discriminator network or adjusting a function associated with one or more nodes of the discriminator network.
14. The method of claim 1 , further comprising retraining the trained discriminator network using the verifications of the true positives and the false positives.
15. The method of claim 1 , wherein the verifications of the true positives and the false positives are received via an input device.
16. A non-transitory computer-readable medium comprising instructions configured to cause at least one processor to:
receive a first plurality of feature indicators for at least one region in a first plurality of image frames, the at least one region including one or more representations of a feature-of-interest;
train a discriminator network using a first training set including the first plurality of image frames and the first plurality of feature indicators;
apply the trained discriminator network to a second plurality of image frames to produce a second plurality of feature indicators for at least one region in the second plurality of image frames, the at least one region including one or more representations of the feature-of-interest;
receive verifications of true positives and false positives with respect to the second plurality of feature indicators; and
train a generative adversarial network using a second training set including the second plurality of image frames and the verifications.
17. The non-transitory computer-readable medium of claim 16 , wherein training the generative adversarial network includes training a generative network to produce artificial representations of the feature-of-interest.
18. The non-transitory computer-readable medium of claim 17 , wherein training the generative adversarial network further includes training an adversarial network to distinguish the artificial representations of the feature-of-interest from real representations of the feature-of-interest.
19. A system for training a generative adversarial network, comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute the instructions to:
receive a first plurality of feature indicators for at least one region in a first plurality of image frames, the at least one region including one or more representations of a feature-of-interest;
train a discriminator network using a first training set including the first plurality of image frames and the first plurality of feature indicators;
apply the trained discriminator network to a second plurality of image frames to produce a second plurality of feature indicators for at least one region in the second plurality of image frames, the at least one region including one or more representations of the feature-of-interest;
receive verifications of true positives and false positives with respect to the second plurality of feature indicators; and
train a generative adversarial network using a second training set including the second plurality of image frames and the verifications.
20. The system of claim 19 , wherein training the generative adversarial network includes training a generative network to produce artificial representations of the feature-of-interest.