Spectrum Analysis Transparency via Contribution Heatmaps
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Solution Overview
Problem
Spectrum analysis methods, such as HPLC and TOF-SIMS, require expertise to separate and analyze test substances from impurities, and machine learning approaches like deep learning, while accurate, lack transparency in data processing, making it difficult to assess result reliability.
Innovation Solution
An information processing apparatus and method that acquires quantitative information on test substances by inputting spectrum information into a learning model, and estimates the degree of contribution of this information, using a regression learning model generated through machine learning, allowing for accurate analysis without extensive knowledge or skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning is used for spectrum analysis, then measurement precision is improved, but reliability of result interpretation deteriorates due to black box data processing
Solution Approach 1:
The patent introduces an explanation generation unit as an intermediary between the deep learning model and the user. This unit generates visual explanations (heatmaps) that show which parts of the spectrum information contributed most to the analysis results, making the black box process transparent while preserving the high accuracy of deep learning
2Reliability
If conventional spectrum analysis methods are used, then reliability of result interpretation is maintained through expert knowledge, but ease of operation deteriorates due to required expertise
Solution Approach 1:
The system performs self-explanation by automatically generating visual interpretations of its own analysis process. The explanation generation unit creates heatmaps that highlight important spectrum regions without requiring external expert intervention, enabling non-experts to understand and verify results independently
3Measurement precision
If machine learning models are trained with extensive data, then measurement precision is improved, but device complexity increases due to large training data requirements
Solution Approach 1:
The patent extracts and displays only the most critical information from the complex model processing. The explanation generation unit identifies and highlights the key spectrum regions that contributed most to the analysis, allowing users to understand results without needing to process or store large amounts of training data
Data Source
AI summary
An information processing apparatus includes information acquisition means configured to acquire quantitative information on a test substance, which is estimated by inputting spectrum information of a sample including the test substance into a learning model, and degree-of-contribution acquisition means configured to acquire a degree of contribution of the acquired quantitative information on the test substance.