Adversarial Network for Prognostic Tile Selection in Whole Slide Images
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Solution Overview
Problem
Current machine-based approaches for predicting disease prognosis from whole slide images (WSIs) fail to account for intra-tumoral heterogeneity, often missing the most prognostic regions within tumors and relying on arbitrary or random sampling methods, which can lead to inaccurate predictions.
Innovation Solution
The sequential integration of adversarial networks with handcrafted features (SANwicH) combines fully convolutional neural networks with biologically driven histomorphometric features to identify regions of prognostic significance within WSIs, using adversarial training to enhance prediction robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If random sampling methods are used to extract regions from WSIs, then the analysis process is simple and fast, but the prediction accuracy deteriorates due to missing prognostic regions and failing to account for intra-tumoral heterogeneity
Solution Approach 1:
The method performs preliminary action by training an adversarial network model in advance to identify prognostic regions. The trained model is then applied to WSIs to selectively extract informative regions before further analysis, ensuring that the most relevant tumor regions are captured while maintaining a systematic and reproducible workflow.
Solution Approach 2:
The adversarial network acts as an intermediary between the WSI and the prognosis prediction system. It processes the entire WSI and generates a significance map that highlights prognostic regions, which are then used as input for subsequent analysis. This intermediary step enables accurate identification of informative regions without requiring manual intervention.
2Reliability
If tissue microarrays or small random regions are used for prognosis prediction, then the processing time and computational resources are reduced, but the reliability deteriorates due to inability to account for intra-tumoral heterogeneity
Solution Approach 1:
The method extracts only the most informative regions from the WSI based on the adversarial network's significance map. Instead of analyzing the entire WSI or using random samples, it selectively extracts regions with high prognostic value, reducing processing time while maintaining reliability by focusing on the most relevant tumor areas.
Solution Approach 2:
The approach applies local quality by treating different regions of the WSI differently. The adversarial network assigns different significance scores to different regions, allowing the system to focus computational resources on regions with high prognostic value while giving less attention to less informative areas, thereby improving reliability without requiring full WSI analysis.
3Measurement precision
If traditional machine-based approaches are used, then the system is easier to implement, but the measurement precision deteriorates due to arbitrary sampling that misses prognostic regions
Solution Approach 1:
The method replaces arbitrary mechanical sampling with an intelligent adversarial network-based selection system. Instead of randomly selecting or systematically sampling regions, the adversarial network learns to identify prognostic regions based on their visual characteristics, substituting mechanical randomness with AI-driven precision while maintaining system automation.
Data Source
AI summary
Embodiments discussed herein facilitate generation of a prognosis for a medical condition based on determination of one or more histomorphometric features for tiles of a whole slide image (WSI) that have been identified as the most prognostically significant tiles of the WSI. A first set of embodiments discussed herein relates to training of a fully convolutional network (FCN) to determine the prognostic significance of pixels of a WSI. A second set of embodiments discussed herein relates to determination of a prognosis based on analysis of regions identified as the most prognostically significant by a trained FCN.