Biological Image Tiling for Accurate Patient Response Prediction
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Solution Overview
Problem
Traditional patient response prediction techniques in clinical trials are inaccurate and computationally inefficient due to the large size of biological images, typically extracting only a few features and requiring billions of parameters, leading to low prediction accuracy and high computational demands.
Innovation Solution
Implementing image processing and machine learning techniques that segment biological images into tiles, using an artificial neural network to classify and weight each tile for abnormal tissue and patient response, allowing for higher-level feature identification and aggregation to predict patient responses with increased accuracy and reduced computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature extraction methods are used on large biological images, then computational simplicity is maintained, but prediction accuracy deteriorates
Solution Approach 1:
The patent divides large biological images into smaller image tiles (e.g., 256x256 pixel tiles). Each tile is independently processed by the neural network to extract relevant features. This segmentation reduces the computational burden on individual processing units while collectively capturing comprehensive tissue characteristics across the entire image, thereby improving prediction accuracy without overwhelming computational resources.
2Measurement precision
If billions of parameters are used in traditional models, then comprehensive feature coverage is achieved, but computational efficiency deteriorates
Solution Approach 1:
The patent extracts only the most relevant features from image tiles using a neural network with a focused parameter set. Instead of processing billions of parameters across entire images, the system extracts key pathological features (e.g., tissue architecture, cellular morphology, staining patterns) from smaller tile regions. This selective extraction maintains comprehensive feature coverage while dramatically reducing computational requirements and improving processing efficiency.
3Measurement precision
If traditional machine learning techniques are used, then implementation simplicity is maintained, but prediction accuracy deteriorates
Solution Approach 1:
The patent introduces an artificial neural network as an intermediary between traditional machine learning techniques and complex image analysis. The neural network serves as a mediator that automatically learns optimal feature representations from image tiles and maps them to patient response predictions. This intermediary layer enables sophisticated pattern recognition and higher prediction accuracy while abstracting away the complexity of manual feature engineering, making the system more accessible and easier to implement.
Data Source
AI summary
Data processing systems for predicting one or more responses to a chemical substance based on biological images. At least some of the data processing systems include at least one processor configured to execute at least one artificial neural network trained to predict one or more responses to a chemical substance based on biological images. When the at least one processor is executing computer-executable instructions, the at least one processor is configured to carry out operations including processing spatially arranged image tile data through one or more data structures storing one or more portions of executable logic included in the artificial neural network to determine one or more responses of a patient to the chemical substance.