Tissue Image Area Selection for Better Estimator Training
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Solution Overview
Problem
Existing biological tissue image processing systems face challenges in enhancing the quality of learning for machine learning-based estimators used in identifying target components within biological tissues, as they often lack efficient methods for determining optimal observation areas and providing high-quality learning images.
Innovation Solution
A biological tissue image processing system that includes a machine learning-based estimator, a determiner for selecting the current observation area based on already observed areas, and a controller to control microscope operations, which uses reference images from low magnification observations to calculate evaluation values and select candidate areas for high magnification observation, thereby providing effective learning-purpose images to the estimator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If various learning-purpose images are provided to increase the generalization performance of the estimator, then the learning quality is improved, but the time and resources required for observation and processing increase
Solution Approach 1:
The system performs low magnification observation to identify candidate observation areas before conducting high magnification observation. This preliminary action filters out irrelevant areas, ensuring that high magnification observation is only performed on promising candidate areas, thus reducing the total observation time while maintaining learning quality.
Solution Approach 2:
The observation process is divided into two stages: low magnification observation to identify candidate areas, and high magnification observation to capture detailed learning-purpose images. This segmentation allows the system to efficiently allocate observation resources across different magnification levels, improving overall efficiency.
2Adaptability or versatility
If high magnification observation is performed on multiple candidate areas to provide diverse learning images, then the generalization performance is improved, but the complexity of determining optimal observation areas increases
Solution Approach 1:
Low magnification observation is performed first to pre-identify candidate observation areas with interesting features before high magnification observation. This preliminary filtering simplifies the subsequent high magnification observation process by limiting it to only those areas that are likely to provide valuable learning data.
Solution Approach 2:
The low magnification observation acts as an intermediary step between the broad tissue overview and the detailed high magnification observation. It bridges the gap by identifying and selecting candidate areas that warrant further detailed observation, thus simplifying the overall process.
3Productivity
If the estimator is trained on images from limited observation areas, then the processing time is reduced, but the estimation accuracy on diverse biological tissue structures deteriorates
Solution Approach 1:
The system performs preliminary low magnification observation to identify diverse candidate areas with different structural characteristics before high magnification observation. This ensures that the selected training images represent a wide variety of biological tissue structures, improving estimation accuracy without requiring observation of the entire tissue sample.
Solution Approach 2:
The system applies different observation strategies to different areas: low magnification for broad survey and high magnification for detailed observation of selected candidate areas. This local differentiation allows efficient resource allocation while ensuring diverse and representative training data is collected.
Data Source
AI summary
A current observation area is determined exploratorily from among a plurality of candidate areas, on the basis of a plurality of observed areas in a biological tissue. A plurality of reference images obtained by means of low-magnification observation of the biological tissue are utilized at this time. A learning image is acquired by means of high-magnification observation of the determined current observation area. A plurality of convolution filters included in an estimator can be utilized to evaluate the plurality of candidate areas.