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US 2024/0185577 A1Published application
Reinforced attention
AssigneeBayer Aktiengesellschaft
InventorsMatthias LENGA, Marvin PURTORAB, Thiago RAMOS DOS SANTOS, Jens HOOGE, Veronica CORONA
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Abstract
The present invention relates to the technical field of machine learning. Subject matter of the present invention is a novel approach for training a neural network and the use of this approach for the processing of (medical) images.
Claims13
1:
- A computer-implemented method of training a machine learning model comprising a transformation agent and a task performing agent to perform a task based on an image the method comprising:
- receiving a training set comprising a multitude of images;
- feeding each image of the training set to the transformation agent, wherein the transformation agent is configured to apply one or more image transformations to each image based, at least in part, on a set of transformation parameters;
- feeding each transformed image to the task performing agent, wherein the task performing agent is configured to perform a task on each transformed image;
- determining, for each task performed by the task performing agent, a task performance loss;
- and
- training the transformation agent to optimize the set of transformation parameters, based, at least in part, on minimizing the task performance loss.
4 more elements
2:
- The method of claim 1, further comprising:
- storing the trained machine learning model and/or outputting the trained machine learning model and/or using the trained machine learning model to perform a task on an image.
3:
- The method of claim 1, further comprising:
- outputting one or more transformed images.
4:
- The method of claim 1, wherein the transformation agent is a reinforcement learning agent.
5:
- The method of claim 1, further comprising:
- providing a machine learning system, the machine learning system comprising an actor, and a critic,
- wherein the actor is configured to select, for each image received, based on a policy, the one or more image transformations,
- wherein the critic is configured to:
- receive the task performance loss and information about the one or more image transformations selected by the actor;
- define a value function based on the task performance loss and the information about the one or more image transformations;
- and
- update the policy of the actor based on the value function.
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6:
- The method of claim 1, wherein the one or more image transformations comprises:
- dilation, shear, translation, scaling, homothety, reflection, rotation, shear mapping, elastic deformation, flipping, scaling, stretching, cropping, resizing, filtering, masking, Fourier transformation, discrete cosine transformation, or combinations thereof.
7:
- The method of claim 1, wherein the one or more image transformations comprises selecting a region of interest, wherein the image is either reduced to the region of interest or masked.
8:
- The method of claim 1, wherein the task is selected from one or more of the following tasks:
- classification, regression, segmentation, reconstruction, image quality enhancement.
9:
- The method of claim 1, wherein each image is or comprises a medical image, in particular a CT scan, an X-ray image, an MRI scan, a fluorescein angiography image, an OCT scan, a histopathological image, or an ultrasound image.
10:
- The method of claim 1, wherein the transformation agent and/or the task performing agent is or comprises an artificial neural network.
11:
- The method of claim 5, wherein the actor, the critic, and the task performing agent are trained in a combined training.
12:
- A computer system comprising:
- a processor;
- and
- a memory storing an application program configured to perform, when executed by the processor, an operation, the operation comprising:
- receiving a training set comprising a multitude of images,
- feeding each image of the training set to a transformation agent, wherein the transformation agent is configured to apply one or more image transformations to each image based, at least in part, on a set of transformation parameters,
- feeding each transformed image to a task performing agent, wherein the task performing agent is configured to perform a task on each transformed image,
- determining, for each task performed by the task performing agent, a task performance loss, and
- training the transformation agent to optimize the set of transformation parameters, based, at least in part, on minimizing the task performance loss.
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13:
- A non-transitory computer-readable storage medium comprising instructions that, when executed by a computer, cause the computer to:
- receive a training set comprising a multitude of images;
- feed each image of the training set to a transformation agent, wherein the transformation agent is configured to apply one or more image transformations to each image based, at least in part, on a set of transformation parameters,
- feed each transformed image to a task performing agent, wherein the task performing agent is configured to perform a task on each transformed image,
- determine, for each task performed by the task performing agent, a task performance loss, and
- train the transformation agent to optimize the set of transformation parameters, based, at least in part, on minimizing the task performance loss.
3 more elements