# US 2025/0095340 A1 · Computer-implemented method, data processing apparatus, and computer program for image analysis

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- Number: US 2025/0095340 A1
- Document ID: US20250095340A1
- Status: Published application
- Kind: A1
- Application number: 18/885,949
- Filed: 2024-09-16
- Published: 2025-03-20
- Earliest claimed: 2023-09-19
- Assignees: Fujitsu Limited
- Applicants: Fujitsu Limited
- Inventors: Serban GEORGESCU; Abhra CHAUDHURI
- Web page: https://patentgrep.com/patent/US20250095340A1

## CPC

- G06N 3/045: Combinations of networks
- G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 16/55: Clustering; Classification
- G06N 3/09: Supervised learning
- G06N 3/094: Adversarial learning
- G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V 10/82: using neural networks
- G06V 20/10: Terrestrial scenes (scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59)
- G16H 30/40: for processing medical images, e.g. editing
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A computer-implemented method of training a machine-learning model for image analysis, the method comprising receiving an input training dataset comprising real training data and augmented training data; iteratively training an encoder of the machine-learning model to obtain a trained encoder, the iterative training comprising: training a discriminator model by minimising a discriminator loss function using the input training dataset to obtain a trained discriminator, the trained discriminator being configured to discriminate whether input data is real data or augmented data; and training the encoder by maximising a discriminator error using the training dataset and the trained discriminator to obtain a trained encoder, the trained encoder being configured to invariantly encode real data and augmented data to a same representation space.

## Claims

### Claim 1

A computer-implemented method of training a machine-learning model for image analysis, the method comprising:
receiving an input training dataset comprising real training data corresponding to a real modality and augmented training data;
iteratively training an encoder of the machine-learning model to obtain a trained encoder, the iterative training comprising:
training a discriminator model by minimising a discriminator loss function using the input training dataset to obtain a trained discriminator, the trained discriminator being configured to discriminate whether input data is real data or augmented data; and
training the encoder by maximising a discriminator error using the training dataset and the trained discriminator to obtain a trained encoder, the trained encoder being configured to invariantly encode real data and augmented data to a real representation space, such that representations preserve information about the real modality.

### Claim 2 (depends on 1)

The method of claim 1, wherein the machine-learning model comprises further downstream layers, the further downstream layers with the trained encoder being configured by training to output analysis results of input data.

### Claim 3 (depends on 2)

The method of claim 2, further comprising iterative training of the further downstream layers by a multi-objective optimisation procedure using the training dataset and the trained encoder to obtain trained further downstream layers.

### Claim 4 (depends on 2)

The method of claim 2, wherein the further downstream layers comprise transformer encoders, reassembly modules, and fusion modules.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
receiving an input image; and
processing the input image using the trained machine-learning model to obtain analysis results of the input image.

### Claim 6 (depends on 1)

The method of claim 1, wherein the machine-learning model is for water image segmentation, the input training dataset comprising real training water images and simulated training water images.

### Claim 7 (depends on 6)

The method of claim 6, further comprising:
receiving an input water image in real-time; and
processing the input water image using the trained machine-learning model to obtain water segmentation results of the input water image.

### Claim 8 (depends on 7)

The method of claim 7, further comprising, responsive to determining that the water segmentation results exceed a threshold, outputting an alarm.

### Claim 9 (depends on 1)

The method of claim 1, wherein the machine-learning model is for medical image segmentation or classification, the input training dataset comprising training medical images and corresponding genomic training data.

### Claim 10 (depends on 9)

The method of claim 9, further comprising:
receiving an input medical image in real-time; and
processing the input medical image using the trained machine-learning model to obtain medical segmentation or classification results of the input medical image.

### Claim 11 (depends on 10)

The method of claim 10, further comprising, responsive to determining that the medical segmentation or classification results exceed a threshold, outputting an alarm.

### Claim 12

A computer implemented method of image analysis, the method comprising:
receiving an input image;
processing the input image using a trained machine-learning model to obtain analysis data of the input image, wherein the trained machine-learning model comprises:
a trained encoder, iteratively trained using an input training dataset comprising real training data corresponding to a real modality and augmented training data, the iterative training having comprised:
training a discriminator model by minimising a discriminator loss function using the input training dataset to obtain a trained discriminator, the trained discriminator being configured to discriminate whether input data is real data or augmented data; and
training the encoder by maximising a discriminator error using the training dataset and the trained discriminator to obtain the trained encoder, the trained encoder being configured to invariantly encode real data and augmented data to a real representation space, such that representations preserve information about the real modality; and
trained further downstream layers, trained by a multi-objective optimization procedure using the training dataset and the trained encoder.

### Claim 13

A data processing apparatus comprising:
a memory storing computer-executable instructions; and
a processor configured to execute the instructions to:
receive an input training dataset comprising real training data corresponding to a real modality and augmented training data;
iteratively train an encoder of the machine-learning model to obtain a trained encoder, the iterative training comprising:
training a discriminator model by minimising a discriminator loss function using the input training dataset to obtain a trained discriminator, the trained discriminator being configured to discriminate whether input data is real data or augmented data; and
training the encoder by maximising a discriminator error using the training dataset and the trained discriminator to obtain a trained encoder, the trained encoder being configured to invariantly encode real data and augmented data to a real representation space, such that representations preserve information about the real modality.

### Claim 14

A computer program comprising instructions which, when the program is executed by a computer, cause the computer to:
receive an input training dataset comprising real training data corresponding to a real modality and augmented training data;
iteratively train an encoder of the machine-learning model to obtain a trained encoder, the iterative training comprising:
training a discriminator model by minimising a discriminator loss function using the input training dataset to obtain a trained discriminator, the trained discriminator being configured to discriminate whether input data is real data or augmented data; and
training the encoder by maximising a discriminator error using the training dataset and the trained discriminator to obtain a trained encoder, the trained encoder being configured to invariantly encode real data and augmented data to a real representation space, such that representations preserve information about the real modality.

### Claim 15

A non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to:
receive an input training dataset comprising real training data corresponding to a real modality and augmented training data;
iteratively train an encoder of the machine-learning model to obtain a trained encoder, the iterative training comprising:
training a discriminator model by minimising a discriminator loss function using the input training dataset to obtain a trained discriminator, the trained discriminator being configured to discriminate whether input data is real data or augmented data; and
training the encoder by maximising a discriminator error using the training dataset and the trained discriminator to obtain a trained encoder, the trained encoder being configured to invariantly encode real data and augmented data to a real representation space, such that representations preserve information about the real modality.