Medical Data Processing Apparatus Online Learning Confidence Interval
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Solution Overview
Problem
Current medical data processing systems face challenges in dynamically updating knowledge for accurate lesion detection and automatic diagnosis, particularly in securing sufficient case data for machine learning and determining the reliability of lesion predictions.
Innovation Solution
The medical data processing apparatus employs online learning to dynamically update knowledge, generates feature and classification parameters based on image data, and calculates a confidence interval to assess the reliability of lesion predictions, allowing for efficient and accurate lesion detection and diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning is applied to lesion detection using past cases as teacher data, then automatic diagnosis capability is improved, but the difficulty of securing sufficient case data for learning increases
Solution Approach 1:
The system performs online learning where the machine learning model automatically updates its knowledge by learning from new case data as it becomes available, without requiring a pre-existing large database of labeled medical images. The model serves itself by continuously adapting to new data patterns, eliminating the need for manual curation of extensive training datasets.
Solution Approach 2:
The system transitions from static pre-trained models to dynamic models that continuously update their parameters in real-time based on incoming case data. The machine learning model adapts its diagnostic capabilities dynamically as new cases are processed, allowing the system to learn from actual clinical practice rather than relying on fixed training data.
2Adaptability or versatility
If online learning is used to dynamically update knowledge, then the ability to adapt to new cases is improved, but the complexity of determining learning completion increases
Solution Approach 1:
The system uses feedback mechanisms where the machine learning model continuously monitors its performance metrics (such as accuracy, precision, and recall) during online learning. This feedback allows the system to automatically determine when learning objectives have been met and when the model has sufficiently adapted to new case patterns, simplifying the complexity of learning completion determination.
3Measurement precision
If feature generating parameters and classification parameters are stored as new diagnostic knowledge, then the accuracy of lesion detection is improved, but the amount of data processing and storage required increases
Solution Approach 1:
The system extracts and stores only the essential diagnostic knowledge and critical features from the processed case data, rather than storing all raw data and processing results. By extracting only the necessary parameters and insights, the system maintains high lesion detection accuracy while significantly reducing data storage and processing requirements.
Data Source
AI summary
The medical data processing apparatus according to any of embodiments includes processing circuitry. The processing circuitry is configured to calculate a probability that target image data has a lesion and a confidence interval indicating a reliability of the probability, and to output data on the lesion based on the probability and the confidence interval of the probability.