Whole-Slide Image Quality Control via Attention-Based Tile Selection
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Solution Overview
Problem
Current machine learning models for whole-slide image analysis lack interpretability and may generate accurate results for irrelevant reasons, leading to inefficiencies in diagnostic processes.
Innovation Solution
Subdivide whole-slide images into tiles, apply a trained machine learning model to generate predictions, determine attention scores for each tile, and select a subset based on these scores to assess biological relevance, generating pass/fail indications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models process all image tiles in whole-slide images to generate diagnostic predictions, then prediction accuracy is improved, but computational time and resources increase significantly
Solution Approach 1:
The patent divides the whole-slide image into multiple tiles and further segments these tiles into sub-tiles. This segmentation allows the model to process smaller units individually, enabling selective attention to relevant regions. The attention mechanism then weights these sub-tiles to focus computational resources on diagnostically important areas rather than processing all tiles uniformly, thus reducing overall computational time while maintaining prediction accuracy.
Solution Approach 2:
The patent applies local quality by using an attention mechanism that assigns different weights to different sub-tiles based on their diagnostic relevance. Regions with higher attention scores (indicating higher biological relevance) are processed with greater detail and weight, while less relevant regions receive reduced processing. This allows the system to allocate computational resources efficiently, focusing on critical areas that most impact prediction accuracy.
2Measurement precision
If machine learning models analyze all regions of whole-slide images, then diagnostic accuracy is improved, but interpretability of model decisions deteriorates
Solution Approach 1:
By segmenting the image into tiles and sub-tiles, the patent creates discrete, manageable units that can be individually evaluated. The attention mechanism then identifies and highlights specific sub-tiles that contribute most to the prediction, making it easier to interpret which regions drove the diagnostic decision. This segmentation transforms a black-box analysis of the entire slide into an interpretable set of focused regional assessments.
Solution Approach 2:
The attention mechanism serves as an intermediary between the raw image data and the final prediction. It generates attention scores that act as interpretable intermediaries, indicating the relative importance of each sub-tile. These attention scores provide a bridge that allows clinicians to understand which regions influenced the prediction, enhancing model interpretability without sacrificing diagnostic accuracy.
3Manufacturing precision
If machine learning models process high-resolution whole-slide images in detail, then diagnostic precision is improved, but computational resources and processing time increase
Solution Approach 1:
The patent segments high-resolution whole-slide images into multiple tiles and further into sub-tiles, reducing the computational burden of processing the entire high-resolution image at once. The attention mechanism then selectively processes and weights the most relevant sub-tiles, maintaining diagnostic precision by focusing computational resources on critical regions rather than uniformly processing all high-resolution data.
Solution Approach 2:
The patent applies partial action by processing only a subset of sub-tiles in detail rather than all tiles uniformly. The attention mechanism identifies the most relevant sub-tiles that require detailed processing, while less relevant sub-tiles receive minimal or no detailed processing. This partial processing approach maintains diagnostic precision for critical regions while significantly reducing overall computational resource consumption.
Data Source
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AI summary
A method involves receiving a whole-slide image, processing it with a machine learning model to generate a prediction, determining attention scores for image tiles, selecting a subset based on these scores, generating a pass/fail indication. A system includes processors and memory to perform these steps. A non-transitory computer-readable medium contains instructions for executing these processes.