View source for 20240005498. METHOD OF GENERATING TRAINED MODEL, MACHINE LEARNING SYSTEM, PROGRAM, AND MEDICAL IMAGE PROCESSING APPARATUS simplified abstract (FUJIFILM Corporation)

=METHOD OF GENERATING TRAINED MODEL, MACHINE LEARNING SYSTEM, PROGRAM, AND MEDICAL IMAGE PROCESSING APPARATUS=


==Organization Name==

[[:Category:FUJIFILM Corporation|FUJIFILM Corporation]]


[[Category:FUJIFILM Corporation]]

==Inventor(s)==

[[:Category:Akira Kudo of Tokyo (JP)|Akira Kudo of Tokyo (JP)]][[Category:Akira Kudo of Tokyo (JP)]]

==METHOD OF GENERATING TRAINED MODEL, MACHINE LEARNING SYSTEM, PROGRAM, AND MEDICAL IMAGE PROCESSING APPARATUS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240005498 titled 'METHOD OF GENERATING TRAINED MODEL, MACHINE LEARNING SYSTEM, PROGRAM, AND MEDICAL IMAGE PROCESSING APPARATUS

==Simplified Explanation==

The abstract describes a learning model that uses a generative adversarial network to generate images in a different domain based on medical images. The model includes a generator and a discriminator, both implemented using convolutional neural networks. The computer acquires training data consisting of medical images from two domains and performs training using this data.

* The learning model uses a generative adversarial network structure.
* The generator is a convolutional neural network that takes a medical image from one domain as input and generates an image in a different domain.
* The discriminator is another convolutional neural network that receives input data consisting of the generated image or a medical image from the second domain, along with coordinate information of the human body.
* The discriminator's role is to determine the authenticity of the input image.
* The computer acquires training data containing medical images from both domains.
* The computer performs training processing using the acquired training data.

== Potential Applications ==
* Medical image translation: The model can be used to generate images in a different domain, such as converting X-ray images to MRI images or vice versa.
* Data augmentation: The generated images can be used to augment existing medical image datasets, increasing the diversity and size of the training data.
* Image synthesis: The model can be used to synthesize realistic medical images for various purposes, such as training other machine learning models or simulating medical scenarios.

== Problems Solved ==
* Limited availability of labeled medical image data: The model can generate synthetic images in a different domain, allowing for the creation of larger and more diverse datasets for training machine learning models.
* Domain adaptation: The model can bridge the gap between different domains of medical images, enabling the transfer of knowledge and insights from one domain to another.
* Image synthesis challenges: The model addresses the challenge of generating realistic and high-quality medical images, which can be useful for various medical imaging applications.

== Benefits ==
* Improved data availability: The model enables the generation of synthetic medical images, reducing the reliance on scarce and expensive labeled data.
* Enhanced training and performance: By augmenting existing datasets and bridging domain gaps, the model can improve the training and performance of machine learning models in medical imaging tasks.
* Cost and time savings: Generating synthetic images can save time and resources compared to collecting and labeling new medical image data.


==Original Abstract Submitted==

by using a learning model having a structure of a generative adversarial network including a first generator configured using a first convolutional neural network that receives an input of a medical image of a first domain and that outputs a first generated image of a second domain, and a first discriminator configured using a second convolutional neural network that receives an input of data including first image data, which is the first generated image or a medical image of the second domain included in a training dataset and coordinate information of a human body coordinate system corresponding to each position of a plurality of unit elements configuring the first image data, and that discriminates authenticity of the input image, a computer acquires a plurality of pieces of training data including the medical image of the first domain and the medical image of the second domain; and performs training processing.



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