Histopathology Biomarker Detection With Tile-Level Deep Learning
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Solution Overview
Problem
Current methods for analyzing histopathology slides, such as IHC staining and traditional deep learning techniques, are inefficient and resource-intensive for accurately identifying cancer-related biomarkers like TILs and PD-L1, due to the high computational requirements and lack of practical annotation methods for large pixel datasets.
Innovation Solution
A deep learning framework is developed to analyze histopathology images using multiscale and single-scale configurations, incorporating tile-level and pixel-level classifiers, and multiple instance learning, to efficiently identify biomarkers like TILs and PD-L1, reducing computational load and annotation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deep learning techniques are used to analyze histopathology slides, then biomarker identification accuracy can be achieved, but computational requirements and resource consumption increase significantly
Solution Approach 1:
The patent segments the histopathology slide into multiple tiles or patches, processing each tile independently through the deep learning model. This divides the large computational task of analyzing the entire high-resolution slide into smaller, more manageable sub-tasks, reducing peak memory usage and computational load while maintaining identification accuracy through aggregate analysis of tile-level predictions
Solution Approach 2:
The patent transitions from pixel-level analysis to tile-level or patch-level analysis, changing the dimensional scale of processing. By representing the slide as a grid of tiles rather than individual pixels, the system reduces the effective data dimensionality and computational complexity while preserving essential diagnostic information through appropriate tile sizing and aggregation strategies
2Reliability
If comprehensive annotation of large pixel datasets is performed, then training data quality improves, but annotation time and resource requirements increase
Solution Approach 1:
The patent segments the annotation task by working with tile-level labels rather than requiring pixel-level annotations. Each tile can be annotated as a whole unit with a single label (e.g., presence/absence of biomarker), dramatically reducing the time and expertise required compared to pixel-level segmentation while still providing sufficient training signal for the deep learning model to learn discriminative features
Solution Approach 2:
The patent uses partial annotation by labeling only the essential tile-level characteristics rather than performing complete pixel-level annotation. This partial action approach provides adequate training data quality for the application's needs without the excessive time investment of full pixel-level annotation, achieving a practical balance between reliability and efficiency
Data Source
AI summary
A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.