Machine Learning Microscope for Biology Data Annotation
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Solution Overview
Problem
The processing of vast amounts of biology-related data, such as images of biological structures, is time-consuming and expensive when done manually, lacking efficient methods for analysis and annotation.
Innovation Solution
A system utilizing one or more processors to apply a trained visual recognition machine-learning algorithm to generate high-dimensional representations of biology-related image-based input data, allowing for semantic similarity mapping and automatic annotation or tagging of images, even if unlabeled.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of biology-related data is performed, then accuracy can be maintained, but processing time and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis with automated machine learning systems. Trained ML models process biology-related images and data automatically, substituting human analysts with computational algorithms that can handle large volumes of data rapidly without manual intervention.
Solution Approach 2:
The system enables self-service processing where the ML models autonomously analyze and annotate data without requiring continuous human oversight. The models serve themselves by automatically processing incoming data streams, generating annotations, and identifying patterns without manual guidance for each data point.
2Productivity
If manual annotation of biology images is performed, then annotation quality can be ensured, but the process becomes expensive and time-consuming
Solution Approach 1:
Manual annotation work is replaced with automated ML-based annotation systems. The patent employs trained machine learning models that automatically generate annotations for biology images, replacing the need for human annotators and significantly reducing both time and cost while maintaining consistent annotation quality.
Solution Approach 2:
The ML models are pre-trained on large datasets before deployment. This preliminary training action enables the models to perform annotation tasks automatically without requiring real-time human intervention, allowing high-throughput processing at reduced cost once the models are deployed.
3Loss of information
If high-dimensional representations with varied values are used, then semantic similarity can be captured, but data storage requirements increase
Solution Approach 1:
The patent transforms image data into high-dimensional vector representations where each dimension captures specific semantic features. This dimensionality change allows the system to preserve semantic similarity information in a structured format, enabling efficient comparison and search while organizing data in a way that balances information retention with storage efficiency.
Data Source
AI summary
A system (100) comprising one or more processors (110) and one or more storage devices (120) is configured to obtain biology-related image-based input data (107) and generate a high-dimensional representation of the biology-related image-based input data (107) by a trained visual recognition machine-learning algorithm executed by the one or more processors (110). The high-dimensional representation comprises at least 3 entries each having a different value. Further, the system is configured to at least one of store the high-dimensional representation of the biology-related image-based input data (107) together with the biology-related image-based input data (107) by the one or more storage devices (120) or output biology-related language-based output data (109) corresponding to the high-dimensional representation.