Stain Normalization for Whole Slide Image Classification
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Solution Overview
Problem
Current deep learning models in digital pathology face challenges in classifying whole slide images due to variations in stain and image appearance across different labs and scanners, leading to inconsistent results and the need for manual preprocessing, which is time-consuming and prone to errors.
Innovation Solution
A deep learning-based system that uses a convolutional neural network to normalize and adapt whole slide images by training on a corpus of images with variations in lightness, hue, saturation, and rotation, allowing for accurate classification across different labs and scanners without requiring a target image for normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional stain normalization methods are used to standardize image appearance across different labs and scanners, then measurement precision is improved, but device complexity and loss of time increase due to manual preprocessing requirements
Solution Approach 1:
The system performs stain normalization and domain adaptation during the training phase by pre-processing training images to match the statistical characteristics of test images from different labs and scanners. This preliminary action embeds the normalization capability into the trained model, eliminating the need for manual preprocessing of new images during deployment.
Solution Approach 2:
The patent replaces manual stain normalization procedures with an automated deep learning-based domain adaptation system. The convolutional neural network automatically learns and applies stain normalization transformations, substituting mechanical/manual image processing with intelligent automated processing that maintains precision while reducing time loss.
2Measurement precision
If deep learning models are trained on data from a single lab to achieve high classification accuracy, then measurement precision is improved, but adaptability deteriorates when applying to images from different labs and scanners
Solution Approach 1:
The system changes the statistical parameters of the training data by applying stain normalization transformations that match the color distribution, lightness, and saturation characteristics of target lab images. This parameter adjustment allows the model trained on one lab's data to generalize effectively to images from different labs and scanners while maintaining high classification accuracy.
3Reliability
If manual stain normalization is performed to account for variations in tissue preparation and staining protocols, then reliability is improved, but device complexity and ease of operation worsen due to requiring expert intervention
Solution Approach 1:
The deep learning system performs stain normalization automatically without requiring manual expert intervention. The convolutional neural network self-adjusts to account for variations in tissue preparation and staining protocols by learning from normalized training data, enabling the system to serve itself in maintaining diagnostic consistency across different image sources.
Data Source
AI summary
Techniques for stain normalization image processing for digitized biological tissue images are presented. The techniques include obtaining a digitized biological tissue image; applying to at least a portion of the digitized biological tissue image an at least partially computer implemented convolutional neural network trained using a training corpus including a plurality of pairs of images, where each pair of images of the plurality of pairs of images includes a first image restricted to a lightness axis of a color space and a second image restricted to at least one of: a first color axis of the color space and a second color axis of the color space, such that the applying causes an output image to be produced; and providing the output image.