Biological Structure Detection with Coregistered Informer Images
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Solution Overview
Problem
The process of interpreting and analyzing microscope images of human tissues or fluids is labor-intensive, expensive, and prone to human error, with limited throughput and subjective variations among pathologists, and existing deep learning models trained on single H&E stain images face challenges due to label noise and ambiguity.
Innovation Solution
A method involving coregistered base and informer images, where the informer image provides complementary information, is used to generate high-quality training labels for machine learning models, enhancing the accuracy and efficiency of biological structure detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually generated annotations based on single H&E stain are used for training deep learning models, then the process is simple and accessible, but the label accuracy is low due to ambiguity and errors
Solution Approach 1:
The patent combines multiple staining protocols (H&E, IHC, IF) to create composite training labels. By merging the information from different staining methods that highlight different biological structures, the system achieves more accurate and comprehensive annotations than any single stain could provide alone.
Solution Approach 2:
The training labels are constructed as composite data structures that integrate information from multiple staining protocols. Each training example comprises coordinated image-stain pairs from different protocols, creating a rich multi-modal training dataset that improves model accuracy.
2Measurement precision
If consecutive tissue slides with different stainings are used to reduce annotation ambiguity, then the label quality improves, but the throughput decreases due to processing multiple slides
Solution Approach 1:
The system uses unsupervised learning algorithms to automatically coregister and align images from different staining protocols without requiring manual intervention. This self-aligning capability eliminates the need for labor-intensive manual registration while maintaining high annotation quality.
Solution Approach 2:
The patent replaces manual pathologist annotation with automated machine learning models that process multiple staining protocols. This substitution of mechanical human labor with computational algorithms dramatically increases throughput while maintaining or improving annotation quality.
3Ease of manufacture
If human experts manually annotate tissue slides, then the analysis can be performed with existing tools, but the process is labor-intensive and expensive
Solution Approach 1:
The patent replaces manual human annotation with automated deep learning models trained on multi-protocol staining data. This substitution eliminates labor-intensive manual review while maintaining or improving annotation accuracy, dramatically increasing throughput and reducing costs.
Solution Approach 2:
The system performs preliminary automated annotation using trained models before any human review is needed. This preliminary action handles the bulk of annotation work automatically, requiring minimal human intervention and significantly speeding up the overall process.
4Measurement precision
If deep learning models are trained on multi-protocol staining data, then the detection accuracy improves, but the computational resources and time required increase
Solution Approach 1:
The system performs preliminary unsupervised coregistration and alignment of images from different staining protocols before training. This preliminary processing organizes the data in advance, making the subsequent training process more efficient and reducing overall computational time.
Solution Approach 2:
The system uses unsupervised learning algorithms to automatically handle image alignment and data integration without manual intervention. This self-service capability eliminates time-consuming manual registration steps while maintaining high detection accuracy.
Data Source
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AI summary
A method for generating a machine learning model for identifying or classifying biological structures depicted in a base image, comprising: obtaining a first training dataset, the first training dataset including first sets of coregistered images of biological structures, each first set including a first base image and a first informer image, and being associated with first label data; training, using the first training dataset, a label generation model, the label generation model configured to accept as input a second set of coregistered images and to produce as output second label data corresponding to the second set of coregistered images, the second set of coregistered images including a second base image and a second informer image; generating a second training dataset using the label generation model, the second training dataset including the second base images and the second label data; and training, using the second training dataset, a classification model configured to accept as input a third base image and to provide as output an indication when a biological structure is identified or classified in the third base image.