Attention-Based Classification of High-Resolution Microscopy Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for classifying high-resolution microscopy images in pathology, such as the sliding window approach, are time-consuming, inconsistent, and require extensive manual annotation and pathologist expertise, limiting scalability and accuracy.
Innovation Solution
An attention-based mechanism is employed to classify whole-slide microscopy images, utilizing a convolutional neural network (CNN) for feature extraction and an attention network for weighting features based on trained characteristics, eliminating the need for bounding box annotations and pathologist insight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sliding window approach with crop classification is used, then classification can be performed on high-resolution images, but the process becomes time-consuming and requires extensive manual annotation
Solution Approach 1:
The patent divides the whole-slide image into multiple tiles or patches, which are then processed in parallel. This segmentation allows the system to maintain high classification accuracy by examining relevant regions while reducing the overall time required compared to processing the entire high-resolution image as a single unit.
Solution Approach 2:
The patent employs a pre-trained classifier that has been previously trained on annotated crop images. This preliminary training action allows the system to perform whole-slide classification without requiring manual annotation during the actual examination process, significantly reducing the time required for new slide analysis.
2Measurement precision
If sliding window approach with heuristic aggregation is used, then whole-slide classification can be achieved, but pathologist expertise is required for developing heuristics
Solution Approach 1:
The patent uses a classifier that processes tiles and aggregates their predictions through a learned function. The system provides feedback by comparing predictions with ground truth labels during training, automatically learning the optimal aggregation strategy without requiring manual heuristic development by pathologists.
Solution Approach 2:
The patent replaces the manual heuristic aggregation process (which requires pathologist expertise) with an automated machine learning-based aggregation function. This substitution eliminates the need for domain expert involvement in developing aggregation rules while maintaining or improving classification accuracy.
3Productivity
If crop classifiers are trained independently, then training data can be processed, but correlations between neighboring windows are not considered
Solution Approach 1:
The patent merges the classification results from multiple independent tile predictions through a learned aggregation function. This combining process captures correlations between neighboring regions by considering the joint distribution of tile predictions, thereby improving overall classification accuracy while maintaining the efficiency of independent tile processing.
Solution Approach 2:
The patent creates a universal aggregation function that works across different tissue types and classification tasks. This multi-functional aggregation mechanism handles correlations between neighboring windows in a general way, improving accuracy without requiring task-specific customization.
4Measurement precision
If manual annotation with bounding boxes is required, then training data can be created, but the process is not scalable
Solution Approach 1:
The patent enables the system to learn from whole-slide level labels without requiring manual bounding box annotations. The model automatically learns to identify and focus on relevant regions through the attention mechanism, making the training process self-sufficient and scalable to large datasets without requiring extensive manual annotation efforts.
Solution Approach 2:
The patent uses pre-trained classifiers and pre-processing steps that reduce the need for manual annotation. By leveraging transfer learning and pre-existing models, the system can achieve high training data quality with minimal manual intervention, thereby improving scalability.
Data Source
AI summary
This invention provides a system and method for analyzing and classifying imaged from whole slides of tissue. A source of image data transmits images of the tissue on the whole slides to a GPU. The GPU performs a feature extraction process that identifies and segments regions of interests in each of the images, and an attention network that, based upon training from an expert, identifies trained characteristics can comprise cancerous and/or pre-cancerous conditions/e.g. those associated with a gastrointestinal tract, such ad Barret's Esophagus. The feature extraction process can include a convolutional neural network (CNN). The attention network can be adapted performs attention/based weighting of features relative to the trained characteristics, and/or the attention network can include 3D convolutional filters. The image data is acquired using an image sensor having approximately 100 Megapixel resolution.