Histological Stain Artifact Classification With Few-Shot Learning

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Solution Overview

Problem

Histological staining processes introduce various artifacts that affect the detection and characterization of biological objects, leading to potential misdiagnosis and inefficiencies in medical diagnosis and treatment selection, and existing machine-learning methods require extensive labeled datasets that are costly and time-consuming to create.

Innovation Solution

Employ few-shot learning and transfer learning techniques to train a deep convolutional neural network using a small number of training samples to classify stain images, including artifacts and other features, enabling accurate classification of histological slide images.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional machine-learning methods are used to classify stain images, then classification accuracy can be improved, but extensive labeled datasets are required which are costly and time-consuming to create

Engineering Contradiction:

Improveclassification accuracyVSAvoidtime to create training data

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent applies transfer learning by pre-training a deep convolutional neural network on a large-scale natural image dataset (e.g., ImageNet) before fine-tuning it on histological stain images. This preliminary training on general image data allows the model to learn robust feature representations that can be transferred to the specific domain of medical imaging, thereby reducing the need for extensive labeled training data while maintaining high classification accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent employs few-shot learning techniques that modify the training approach by using a small number of labeled examples per class. The method changes the parameter of training data quantity from requiring thousands of labeled images to functioning effectively with only a few examples per category, while achieving comparable classification performance through techniques like metric learning and prototype-based classification

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If traditional machine-learning methods are used to classify stain images, then classification accuracy can be improved, but the cost of creating extensive labeled datasets increases

Engineering Contradiction:

Improveclassification accuracyVSAvoidlabeled training data volume

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent uses few-shot learning approaches that create virtual or synthetic training examples through data augmentation techniques, including geometric transformations, color jittering, and mixing of training images. This copying and transformation of limited labeled data creates expanded training sets without requiring additional manual annotation, effectively reducing the need for large volumes of original labeled data

Inventive Principle:

Principle #26Copying

3Illumination intensity

If histological staining processes are used to prepare samples, then biological objects can be visualized, but artifacts are introduced that affect detection and characterization

Engineering Contradiction:

Improvecontrast enhancementVSAvoidstaining artifacts

Core Design Contradiction:

Illumination intensityVSObject-generated harmful factors

Solution Approach 1:

The patent trains the deep learning model to recognize and classify staining artifacts as distinct categories alongside biological structures. By incorporating artifact classification into the training process using labeled data that includes artifact examples, the system converts the harmful effect of artifacts into a beneficial classification capability, enabling the model to distinguish between genuine biological features and artifacts, thereby improving overall diagnostic accuracy

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

Data Source

PatentUS12450927B2Histological stain pattern and artifacts classification using few-shot learning

Publication Date: 2025.10.21 VENTANA MEDICAL SYSTEMS INC

AI summary

A method and system for classifying field of view (FOV) images of histological slides into various categories that include certain stain patterns, artifacts, and/or other features of interest are provided herein. Few-shot learning (e.g., a prototypical network) techniques are used to train a deep convolutional neural network using a small number of training samples for a small number of image classes for classifying stain images belonging to a larger number of image classes.