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1. WO2019052810 - TRAINING A MODEL

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

CLAIMS

1. A computer implemented method (200) of training a model comprising:

receiving (202) a first user input to annotate a first parameter in a portion of data; using (204) a first model to predict an annotation for at least one other parameter of the portion of data based on the received first user input for the first parameter; and

using (206) the annotated first parameter, the predicted annotation of the at least one other parameter and the portion of data as training data to train a second model.

2. A method as in claim 1 wherein the second model is for annotating the first parameter and the at least one other parameter in a further portion of data.

3. A method as in claim 1 or 2 further comprising:

forming a training set of training data for training the second model by repeating: receiving a first user input; and

using a first model to predict an annotation;

for a plurality of portions of data.

4. A method as in any one of claims 1 , 2 or 3 wherein using a first model to predict an annotation is further based on the portion of data.

5. A method as in any one of claims 1 to 4 further comprising:

receiving a second user input providing an indication of an accuracy of the predicted annotation of the at least one other parameter; and

using the indication of the accuracy of the predicted annotation as training data to train the second model.

6. A method as in claim 5 further comprising updating the first model based on the received second user input and the predicted annotation of the at least one other parameter.

7. A method as in any one of claims 1 to 4 wherein using a first model to predict an annotation comprises:

using the first model to provide a plurality of suggestions for the annotation of the at least one other parameter; and

wherein the method further comprises:

receiving a third user input indicating an accuracy of at least one of the plurality of suggestions; and

using the indicated accuracy of the at least one of the plurality of suggestions as training data to train the second model.

8. A method as in claim 7 further comprising updating the first model based on the received third user input and the plurality of suggestions.

9. A method as in any one of the preceding claims wherein the predicted annotation of the at least one other parameter is based on confidence levels calculated by the first model.

10. A method as in any one of the preceding claims wherein:

the portion of data comprises an image;

the first parameter represents a location of a first feature in the image; and the at least one other parameter represents locations of one or more other features in the image.

11. A method as in any one of claims 1 to 9 wherein:

the portion of data comprises a sequence of images separated in time;

the first parameter relates to a first image in the sequence of images; and

the first model predicts an annotation of the first parameter and/ or the at least one other parameter of the portion of data in a second image in the sequence of images, wherein the second image is a different image to the first image.

12. A method as in any one of the preceding claims wherein the portion of data comprises medical data.

13. A method as in any one of the preceding claims wherein the first and/ or second model comprises a deep neural network.

14. A computer program product comprising a non- transitory computer readable medium, the computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method of any one of the preceding claims.

15. A system (100) comprising:

a memory (106) comprising instruction data representing a set of instructions; a processor (102) configured to communicate with the memory ( 06) and to execute the set of instructions, wherein the set of instructions, when executed by the processor (102), cause the processor (102) to:

receive a first user input to annotate a first parameter in a portion of data;

use a first model to predict an annotation for at least one other parameter of the portion of data based on the received first user input for the first parameter; and

use the annotated first parameter, the predicted annotation of the at least one other parameter and the portion of data as training data to train a second model.