Frame-Based Validation for Pathology Model Benchmarking
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Solution Overview
Problem
Current methods for evaluating cellular, molecular, and architectural features in histology samples are highly variable and lack standardization, making it difficult to reliably assess and validate algorithmic approaches for cancer treatment, such as PD-L1 immunohistochemistry expression in urothelial cancer.
Innovation Solution
A platform is developed to collect ground truth reference annotations from a crowd-sourced network of pathologists to measure pathologist performance and validate deep learning models by generating frames from pathology images, processing them with a trained model, and determining the degree of association between reference annotations and model predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pathologists perform manual and categorical scoring of histological features, then the assessment can be completed with current methods, but the variability between pathologists and over time is highly variable and lacks standardization
Solution Approach 1:
The patent replaces manual mechanical scoring by pathologists with automated image analysis algorithms and machine learning models that process histology images. This substitution eliminates human variability and provides consistent, reproducible measurements of histological features such as PD-L1 expression, nuclear grade, and cellular morphology across different samples and time points.
Solution Approach 2:
The patent creates digital copies and representations of histological features through algorithmic analysis. Instead of relying on subjective human interpretation, the system generates quantitative digital measurements and classifications that can be consistently reproduced and validated, thereby improving measurement precision while maintaining reliability.
2Measurement precision
If quantitative measures of PD-L1 expression are used, then standardization and reproducibility improve, but it is difficult to use pathologists' slide-level assessment as ground truth for concordance studies
Solution Approach 1:
The patent performs preliminary quantitative analysis at the frame level before final slide-level assessment. By breaking down whole-slide images into smaller frames and performing automated analysis on each frame, the system generates intermediate quantitative results that can be aggregated to produce final slide-level measurements. This preliminary action creates a bridge between quantitative frame-level measures and traditional slide-level assessment, enabling validation without requiring complex concordance studies.
Solution Approach 2:
The patent segments whole-slide histology images into smaller manageable frames for independent analysis. This segmentation allows quantitative measures to be computed at the frame level and then aggregated to produce slide-level results. The segmentation approach simplifies the validation process by enabling frame-level ground truth establishment that can be systematically combined, rather than requiring complex whole-slide concordance studies among multiple pathologists.
3Area of stationary object
If whole-slide histology images are analyzed, then comprehensive assessment is achieved, but the scale and complexity present challenges to benchmarking algorithms against human performance
Solution Approach 1:
The patent divides large whole-slide images into smaller frames that can be processed independently by algorithms. This segmentation reduces the computational burden and makes it feasible to benchmark algorithm performance against human annotators on a frame-by-frame basis. The framework allows comprehensive whole-slide assessment by aggregating frame-level results while maintaining the ability to evaluate and validate algorithm performance on manageable subsets of the data.
Solution Approach 2:
The patent employs a multi-level analysis approach where algorithms perform analysis on frames (partial action) rather than requiring complete whole-slide analysis for benchmarking purposes. This partial action approach allows for practical validation of algorithm performance against human annotators on representative subsets of frames, while the framework can be scaled to provide comprehensive whole-slide assessment when needed. The excessive action component involves generating detailed frame-level annotations that exceed minimum requirements, providing rich data for validation.
Data Source
AI summary
In some aspects, the described systems and methods provide for validating performance of a model trained on a plurality of annotated pathology images. In validating the trained model, frames may be sampled from one or more pathology images. Each frame may include a distinct portion of a pathology image. Reference annotations on the frames may be received from a plurality of users, each reference annotation describing at least one of a plurality of tissue or cellular characteristic categories or other biological objects for a frame. The frames may be processed using the trained model to generate model predictions, each model prediction describing at least one of the tissue or cellular characteristic categories for a processed frame. Performance of the trained model may be validated based on associating the model predicted annotations with the reference annotations across the one or more pathology images from the plurality of users.