# US 2026/0087618 A1 · Image classification using machine learning including weakly-labeled data

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- Number: US 2026/0087618 A1
- Document ID: US20260087618A1
- Status: Published application
- Kind: A1
- Application number: 19/111,030
- Filed: 2023-09-15
- Published: 2026-03-26
- Earliest claimed: 2022-09-16
- Assignees: Ohio State Innovation Foundation
- Applicants: Ohio State Innovation Foundation
- Inventors: Asmaa ALJUHANI; Raghu MACHIRAJU; Jany CHAN; David LIEBNER; Ishya CASUKHELA
- Web page: https://patentgrep.com/patent/US20260087618A1

## CPC

- G06T 7/0012: {Biomedical image inspection}
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/047: Probabilistic or stochastic networks
- G06N 3/048: Activation functions
- G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/09: Supervised learning
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06T 7/11: Region-based segmentation
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/82: using neural networks
- G06V 20/698: {Matching; Classification}
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G16H 30/40: for processing medical images, e.g. editing
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06T 2207/10056: Microscopic image
- G06T 2207/20021: Dividing image into blocks, subimages or windows
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro
- G06T 2207/30096: Tumor; Lesion
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A system may receive a plurality of digital histology images, wherein each of the plurality of digital histology images is labeled with a respective image-level classification. A system may extract a plurality of tiles from each of the plurality of digital histology images. A system may create a first dataset comprising the plurality of tiles and respective image-level classifications. A system may train a first machine learning model using the first dataset. A system may create a second dataset by sampling the first dataset based on respective classifications and respective uncertainty measures for each of the plurality of tiles output by the trained first machine learning model. A system may train a second machine learning model using the second dataset, wherein the trained second machine learning model is configured to classify one or more tiles of a digital histology image.

## Claims

### Claim 1

A computer-implemented method for training a machine learning model comprising:
receiving a plurality of digital histology images, wherein each of the plurality of digital histology images is labeled with a respective image-level classification;
extracting a plurality of tiles from each of the plurality of digital histology images, wherein each of the plurality of tiles is labeled with a respective image-level classification associated with its respective digital pathology image;
creating a first dataset comprising the plurality of tiles and the respective image-level classifications;
training a first machine learning model using the first dataset, wherein the trained first machine learning model is configured to output a respective classification and a respective uncertainty measure for each of the plurality of tiles;
creating a second dataset by sampling the first dataset based on the respective classifications and the respective uncertainty measures output by the trained first machine learning model; and
training a second machine learning model using the second dataset, wherein the trained second machine learning model is configured for classify one or more tiles of a digital histology image.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, wherein the second dataset comprises a subset of the plurality of tiles of the first dataset.

### Claim 3 (depends on 2)

The computer-implemented method of claim 2, wherein the subset of the plurality of tiles of the first dataset comprises the most diagnostically relevant tiles among the plurality of tiles of the first dataset.

### Claim 4 (depends on 1)

The computer-implemented method of claim 1, wherein the step of creating the second dataset by sampling the first dataset comprises identifying one or more of the plurality of tiles of the first dataset having relatively high predictive probability and relatively low uncertainty.

### Claim 5 (depends on 4)

The computer-implemented method of claim 4, wherein the one or more of the plurality of tiles of the first dataset having relatively high predictive probability and relatively low uncertainty are identified using a threshold value for predictive probability and uncertainty.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, wherein the first machine learning model is a deep learning model.

### Claim 7 (depends on 6)

The computer-implemented method of claim 6, wherein the deep learning model is a convolutional neural network (CNN), wherein the CNN comprises a plurality of fully connected layers and a plurality of dropout layers, wherein each of the plurality of dropout layers corresponds to a respective fully connected layer.

### Claim 8 (depends on 7)

The computer-implemented method of claim 7, wherein the step of training the first machine learning model using the first dataset comprises:
inputting the first dataset into the CNN; and
processing, using the CNN, the first dataset to minimize or maximize an objective function for the CNN.

### Claim 9 (depends on 8)

The computer-implemented method of claim 8, wherein the step of training the first machine learning model using the first dataset further comprises:
inputting the first dataset into the CNN;
predicting, using the CNN, the respective classifications for each of the plurality of tiles; and
obtaining the respective uncertainty measures for each of the plurality of tiles.

### Claim 10

(canceled)

### Claim 11 (depends on 1)

The computer-implemented method of claim 1, wherein the second machine learning model is a deep learning model.

### Claim 12 (depends on 11)

The computer-implemented method of claim 11, wherein the deep learning model is a convolutional neural network (CNN), wherein the CNN comprises a plurality of fully connected layers and a plurality of dropout layers, wherein each of the plurality of dropout layers corresponds to a respective fully connected layer.

### Claim 13 (depends on 12)

The computer-implemented method of claim 12, wherein the step of training the second machine learning model using the second dataset comprises:
inputting the second dataset into the CNN; and
processing, using the CNN, the second dataset to minimize or maximize an objective function for the CNN.

### Claim 14 (depends on 1)

The computer-implemented method of claim 1, wherein the digital histology images are whole slide images.

### Claim 15

(canceled)

### Claim 16 (depends on 14)

The computer-implemented method of claim 14, wherein the whole slide images are images of tissue slices immunostained for different markers.

### Claim 17

(canceled)

### Claim 18

A computer-implemented method for classifying one or more tiles of a digital histology image using a machine learning model comprising:
deploying a machine learning model;
receiving a digital histology image;
extracting a plurality of tiles from the digital histology image;
inputting the plurality of tiles into the deployed machine learning model; and
outputting, using the deployed machine learning model, a respective classification, and a respective uncertainty measure for each of the plurality of tiles.

### Claim 19 (depends on 18)

The computer-implemented method of claim 18, wherein the deployed machine learning model is a deep learning model.

### Claim 20 (depends on 19)

The computer-implemented method of claim 19, wherein the deep learning model is a convolutional neural network (CNN), wherein the CNN comprises a plurality of fully connected layers and a plurality of dropout layers, wherein each of the plurality of dropout layers corresponds to a respective fully connected layer.

### Claim 21 (depends on 20)

The computer-implemented method of claim 20, wherein the step of outputting, using the deployed machine learning model, the respective classification, and the respective uncertainty measure for each of the plurality of tiles comprises:
inputting each of the plurality of tiles into the CNN;
predicting, using the CNN, the respective classifications for each of the plurality of tiles; and
obtaining the respective uncertainty measures for each of the plurality of tiles.

### Claim 22 (depends on 21)

The computer-implemented method of claim 21, wherein the step of obtaining the respective uncertainty measures for each of the plurality of tiles comprises using a Monte Carlo simulation.

### Claim 23 (depends on 18)

The computer-implemented method of claim 18, further comprising generating a classification map for the digital histology image using the respective classifications and the respective uncertainty measures for each of the plurality of tiles output by the deployed machine learning model.

### Claim 24 (depends on 18)

The computer-implemented method of claim 18, wherein the digital histology images are whole slide images.

### Claim 25

(canceled)

### Claim 26 (depends on 24)

The computer-implemented method of claim 24, wherein the whole slide image is an image of a tissue slice immunostained for different markers.

### Claim 27

(canceled)

### Claim 28

(canceled)