Digital Pathology Image Heterogeneity Assessment via Deep Learning
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Solution Overview
Problem
Current methods for identifying histologies and gene mutations in digital pathology images of non-small cell lung cancer (NSCLC) are manual, time-consuming, prone to human error, and cannot assess tumor heterogeneity effectively, limiting accurate diagnosis and treatment recommendations.
Innovation Solution
A computer-implemented method using a deep-learning neural network to classify image features and generate labels for digital pathology images, subdividing images into patches, and computing heterogeneity metrics to assess tissue samples, enabling automated identification of histologies and mutations, and providing visualizations for diagnosis and treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of histologies and gene mutations is performed by pathologists, then diagnostic accuracy can be maintained through expert judgment, but the process becomes time-consuming and laborious
Solution Approach 1:
The patent replaces the manual mechanical process of pathologist review with an automated machine learning system that processes digital pathology images. The system uses trained models to automatically identify histologies and predict gene mutations, substituting human manual analysis with computational algorithms that operate faster while maintaining diagnostic accuracy.
Solution Approach 2:
The patent creates digital copies of physical pathology slides through whole slide imaging, allowing multiple analyses of the same sample without requiring repeated manual examination. The digital copies can be processed by automated systems and reviewed multiple times, eliminating the time loss associated with physical slide handling and re-examination.
2Reliability
If manual review of digital pathology images is performed, then human expertise can be applied to complex cases, but human errors and subjectivity may occur
Solution Approach 1:
The patent replaces human manual review with automated machine learning analysis to eliminate human errors and subjectivity. The system uses consistent algorithmic criteria across all cases, removing variability in human interpretation while maintaining diagnostic reliability through trained models that have been validated on diverse datasets.
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning system's predictions can be reviewed and corrected by pathologists, and these corrections feed back into the system to improve future performance. This creates a closed-loop system that combines automated consistency with human expertise, reducing errors while maintaining reliability.
3Productivity
If automated machine learning techniques are used to identify features in digital pathology images, then productivity and speed are improved, but device complexity increases
Solution Approach 1:
The patent segments the complex task of pathological analysis into distinct modules: whole slide image acquisition, patch extraction, feature detection, histology classification, and mutation prediction. Each module can be independently optimized and validated, managing system complexity while maintaining high processing speed and productivity.
4Loss of information
If comprehensive feature identification is performed to assess tumor heterogeneity, then understanding of tumor biology improves, but the complexity of analysis increases
Solution Approach 1:
The patent divides the tissue sample into multiple patches and analyzes each patch independently for different features (histologies, mutations, morphology). This segmentation allows comprehensive characterization of tumor heterogeneity across the entire sample while managing analysis complexity through localized processing of smaller image regions.
Solution Approach 2:
The patent develops a multi-functional machine learning system that can simultaneously identify multiple types of features (different histologies, various gene mutations, morphological characteristics) from the same input images. This universal system assesses tumor heterogeneity comprehensively without requiring separate specialized analyses for each feature type.
Data Source
AI summary
In one embodiment, a method includes, receiving a digital pathology image of a tissue sample and subdividing the digital pathology image into a plural in of patches. For each patch of the plurality of patches, the method includes identify an image feature detected in the patch and generating one or more labels corresponding to the image feature identified in the patch using a machine-learning model. The method includes determining, based on the generated labels, a heterogeneity metric for the tissue sample. The method includes generating an assessment of the tissue sample based on the heterogeneity metric.