Provided are a machine learning method based on weakly supervised learning includes extracting feature maps about a dataset given a first type of information and not given a second type of information by using a convolutional neural network (CNN), updating the CNN by back-propagating a first error value calculated as a result of performing a task corresponding to the first type of information by using a first model, and updating the CNN by back-propagating a second error value calculated as a result of performing the task corresponding to the first type of information by using a second model different from the first model, wherein the second type of information is extracted when the task corresponding to the first type of information is performed using the second model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning method based on weakly supervised learning, the method performed by a machine learning apparatus and comprising: extracting feature maps about a dataset given a first type of information and not given a second type of information by using a convolutional neural network (CNN); updating the CNN by back-propagating a first error value calculated as a result of performing a task corresponding to the first type of information by using a first model; and updating the CNN by back-propagating a second error value calculated as a result of performing the task corresponding to the first type of information by using a second model different from the first model, wherein the second type of information is extracted when the task corresponding to the first type of information is performed using the second model. 2. The method of claim 1, wherein a weight of a filter of the CNN is initialized to an arbitrary value. 3. The method of claim 1, wherein the updating of the CNN by back-propagating the first error value calculated as a result of performing the task corresponding to the first type of information comprises: determining whether the first error value satisfies a preset reference value; and updating the CNN by back-propagating the second error value calculated as a result of performing the task corresponding to the first type of information only when the first error value satisfies the preset reference value. 4. The method of claim 1, further comprising determining the second type of information about the dataset by using the second model. 5. The method of claim 4, wherein the updating of the CNN by back-propagating the second error value calculated as a result of performing the task corresponding to the first type of information comprises: determining whether the second error value satisfies a preset reference value; and determining the second type of information about the dataset by using the second model only when the second error value satisfies the preset reference value. 6. A machine learning method based on weakly supervised learning, the method performed by a machine learning apparatus and comprising: extracting feature maps about a dataset given a first type of information and not given a second type of information by using a CNN; calculating a first error value by performing a task corresponding to the first type of information by using a first model; and calculating a second error value by performing the task corresponding to the first type of information by using a second model different from the first model; calculating a third error value by applying a weight to each of the first error value and the second error value; and updating the CNN by back-propagating the third error value, wherein the second type of information is extracted when the task corresponding to the first type of information is performed using the second model. 7. The method of claim 6, further comprising adjusting the weight to reflect relative importance of each of the first model and the second model. 8. The method of claim 6, wherein a weight of a filter of the CNN is initialized to an arbitrary value. 9. A machine learning method based on weakly supervised learning, the method performed by a machine learning apparatus and comprising: extracting feature maps about a dataset given object class information and not given object location information by using a CNN; updating the CNN by back-propagating a first error value calculated as a result of performing an object classification task by using an object classifier; and updating the CNN by back-propagating a second error value calculated as a result of performing the object classification task by using an object localizer, wherein the object location information is extracted when the object classification task is performed using the object localizer. 10. The method of claim 9, wherein the object class information is information about the presence or absence of a lesion, and the object location information is location information of the lesion. 11. The method of claim 9, wherein a weight of a filter of the CNN is initialized to an arbitrary value. 12. The method of claim 9, wherein a dataset is a medical dataset. 13. The method of claim 9, wherein the updating the CNN by back-propagating a first error value calculated as a result of performing an object classification task comprises: determining whether the first error value satisfies a preset reference value; and updating the CNN by back-propagating the second error value calculated as a result of performing the object classification task by using the object localizer only when the first error value satisfies the preset reference value. 14. The method of claim 9, further comprising determining the object location information about the dataset by using the object localizer. 15. The method of claim 14, wherein the updating the CNN by back-propagating a second error value calculated as a result of performing the object classification task comprises: determining whether the second error value satisfies a preset reference value; and determining the object location information about the dataset by using the object localizer only when the second error value satisfies the preset reference value. 16. A machine learning apparatus based on weakly supervised learning, the apparatus comprising: one or more processors; a memory which loads a computer program executed by the processors; and a storage, wherein the computer program comprises: a feature map extraction operation of extracting feature maps about a dataset given a first type of information and not given a second type of information by using a CNN; a first learning operation of updating the CNN by back-propagating a first error value calculated as a result of performing a task corresponding to the first type of information by using a first model; and a second learning operation of updating the CNN by back-propagating a second error value calculated as a result of performing the task corresponding to the first type of information by using a second model different from the first model, wherein the second type of information is extracted when the task corresponding to the first type of information is performed using the second model in the second learning operation. 17. The apparatus of claim 16, wherein a weight of a filter of the CNN is initialized to an arbitrary value. 18. The apparatus of claim 16, wherein the first learning operation determines whether the first error value satisfies a preset reference value and updates the CNN by back-propagating the second error value calculated as a result of performing the task corresponding to the first type of information only when the first error value satisfies the preset reference value. 19. The apparatus of claim 16, further comprising a second information determination operation of determining the second type of information about the dataset by using the second model. 20. The apparatus of claim 19, wherein the second learning operation determines whether the second error value satisfies a preset reference value and determines the second type of information about the dataset by using the second model only when the second error value satisfies the preset reference value.