Histopathology Survival Scoring With End-to-End Tile Aggregation
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Solution Overview
Problem
Current survival models for histopathology-based cancer prognosis struggle to effectively stratify patient cohorts into distinct risk groups and fail to systematically map tissue morphology to patient prognosis due to the limitations of two-stage training frameworks, especially in rare cancers like intrahepatic cholangiocarcinoma.
Innovation Solution
The EPIC-Survival model employs an end-to-end deep learning approach that integrates tile encoding and aggregation with stratification boosting, using a deep convolutional neural network to directly produce survival risk scores from whole slide images, enhancing the model's ability to discriminate between risk groups and identify specific histologic features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If two-stage training frameworks are used for survival models, then model training is simpler and more modular, but the model fails to effectively stratify patient cohorts and map tissue morphology to prognosis
Solution Approach 1:
The patent merges the tile encoding module and aggregation module into a unified end-to-end training framework. The tile encoder extracts features from histopathology tiles while the aggregator combines these features to predict survival outcomes, with both modules trained simultaneously using a combined loss function that includes survival loss and stratification loss components, enabling effective patient cohort stratification and prognosis mapping
Solution Approach 2:
The patent introduces stratification boosting by modifying the loss function parameters to include both survival prediction accuracy and cohort stratification quality. The composite loss function balances these objectives through weighted components, allowing the model to optimize for both prognostic accuracy and risk group separation simultaneously
2Adaptability or versatility
If traditional survival models are applied to rare cancers, then general applicability is maintained, but prognostic performance is insufficient due to limited data
Solution Approach 1:
The patent segments the histopathology whole slide images into multiple tiles, extracting features from individual tissue regions. This segmentation approach allows the model to learn from localized morphological patterns even when overall dataset size is limited, improving prognostic performance for rare cancers by focusing on discriminative tissue features rather than requiring large numbers of complete slides
Solution Approach 2:
The patent transforms the problem from slide-level analysis to tile-level feature extraction followed by aggregation. By operating at the tile dimension and then aggregating features to predict survival outcomes, the model effectively increases the sample size through multiple extractable features from each slide, improving statistical power for rare cancer applications
3Measurement precision
If deep learning models are trained to identify histologic features, then prognostic discrimination improves, but model complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex task of prognosis prediction into two manageable segments: a tile encoder that extracts features from individual histopathology tiles using convolutional neural networks, and an aggregator that combines these features to predict survival outcomes. This segmentation reduces overall model complexity while maintaining high discriminative accuracy for risk group classification
Data Source
AI summary
Presented herein are systems and methods for determining scores from biomedical images. A computing system may identify a plurality of tiles in a first biomedical image derived from a sample of a subject. Each tile may correspond to features of the sample. The computing system may apply the plurality of tiles to a machine learning (ML) model. The ML model may include: an encoder to generate a plurality of feature vectors based on the plurality of tiles; a clusterer to select a subset from the plurality of feature vectors; and an aggregator to determine a first score indicative of a time to an event for the subject resulting from the features of the sample. The model may be trained in accordance with a loss derived from second scores determined for second biomedical images. The computing system may store an association between the score and the first biomedical image.