Adaptive Sampling for Whole-Slide Histopathology Image Analysis
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Solution Overview
Problem
Conventional approaches to histopathology image analysis, particularly for whole-slide images, are limited by their inability to efficiently process large images due to computational constraints, leading to impractical analysis times and reliance on expert human pathologists for invasive tumor delineation in breast cancer diagnosis.
Innovation Solution
The implementation of high-throughput gradient-based adaptive sampling using quasi-Monte Carlo sampling and a representation learning classifier based on convolutional neural networks (CNNs) for efficient analysis of whole-slide images, allowing for iterative refinement of invasive breast cancer probability maps and reducing computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CNN approaches are applied to whole-slide images, then image analysis accuracy is improved, but computational time and resource requirements increase exponentially
Solution Approach 1:
The patent divides the large whole-slide image into multiple smaller tiles or patches that can be processed independently by the CNN. This segmentation allows the system to analyze large images by breaking them into manageable units, reducing computational time while maintaining accuracy through subsequent aggregation of tile-level predictions to slide-level conclusions
Solution Approach 2:
The patent introduces a spatial hierarchy dimension by organizing tiles into super-tiles and further into regional aggregates. This multi-scale dimensional organization allows efficient processing at different resolution levels, enabling fast screening at coarse levels and detailed analysis only where needed, thus reducing overall computational time
2Measurement precision
If conventional CNN approaches are applied to whole-slide images, then image analysis accuracy is improved, but computational resource requirements increase beyond practical limits
Solution Approach 1:
By segmenting the WSI into tiles that fit standard CNN input sizes, the patent reduces memory requirements and computational complexity. Each tile is processed independently with standard computational resources, and results are aggregated to achieve slide-level analysis without requiring excessive memory or processing power
Solution Approach 2:
The patent implements multi-scale analysis where not all regions are analyzed at the finest resolution. Coarse-level analysis is performed first to identify regions of interest, and detailed fine-level analysis is applied only to selected areas, reducing overall computational resource requirements while maintaining diagnostic accuracy
3Measurement precision
If expert human pathologists perform invasive tumor delineation, then diagnostic accuracy is improved, but analysis throughput and reproducibility worsen
Solution Approach 1:
The patent implements automated CNN-based systems that perform tumor delineation and classification without requiring expert human pathologist intervention for each case. The system processes slides autonomously, achieving high throughput while maintaining diagnostic accuracy through carefully designed architectures and training protocols
Solution Approach 2:
The patent incorporates iterative refinement where initial CNN predictions are evaluated and refined through multiple processing stages. Feedback from intermediate results guides subsequent analysis, allowing the system to achieve expert-level accuracy through automated multi-stage processing rather than single-pass human review
4Measurement precision
If conventional CNN approaches are applied to whole-slide images, then detailed analysis is improved, but processing speed decreases to impractical levels
Solution Approach 1:
The patent segments the WSI into overlapping or non-overlapping tiles that are processed in parallel. This allows detailed analysis of multiple regions simultaneously, maintaining high processing detail while achieving practical processing speeds through parallel computation and efficient tile-level independent processing
Solution Approach 2:
The patent implements iterative processing where analysis is performed in multiple passes or stages. Coarse filtering is applied first to identify candidate regions, followed by periodic refinement passes that apply detailed analysis only where needed, achieving both detail and speed through staged periodic processing
Data Source
AI summary
Methods, apparatus, and other embodiments associated with classifying a region of tissue represented in a digitized whole slide image (WSI) using iterative gradient-based quasi-Monte Carlo (QMC) sampling. One example apparatus includes an image acquisition circuit that acquires a WSI of a region of tissue demonstrating cancerous pathology, an adaptive sampling circuit that selects a subset of tiles from the WSI using an iterative QMC Sobol sequence sampling approach, an invasiveness circuit that determines a probability of a presence of invasive pathology in a member of the subset of tiles, a probability map circuit that generates an invasiveness probability map based on the probability, a probability gradient circuit that generates a gradient image based on the invasiveness probability map, and a classification circuit that classifies the region of tissue based on the probability map. A prognosis or treatment plan may be provided based on the classification of the WSI.