Semantic Comparison Space for Multi-Modal Data Similarity
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Solution Overview
Problem
Existing methods for determining data similarity, particularly in medical contexts, struggle to accurately calculate similarity between data from different modalities, such as text and image, leading to low accuracy and poor performance in finding similar patients.
Innovation Solution
A method and device for data similarity determination that involves acquiring data from different modalities, mapping this data into a semantic comparison space, and calculating similarity based on the mapped semantic representations, allowing for the utilization of information complementarity and verification between different modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from different modalities (text and image) are directly compared using existing methods, then the computational process is simple, but the similarity calculation accuracy is low
Solution Approach 1:
The patent introduces a semantic comparison space as an intermediary framework that enables accurate comparison between different data modalities. This semantic space acts as a mediator that translates text and image data into a common representation format, allowing for meaningful similarity calculations while maintaining system manageability through modular architecture including mapping modules and comparison modules
Solution Approach 2:
The patent transforms multi-modal data (text and image) into a unified semantic comparison space, effectively adding a new dimensional framework for data representation. This dimensional transformation allows data from different modalities to be projected into a common space where similarity can be accurately measured using geometric or algebraic relationships
2Measurement precision
If only single modality data is used for similarity determination, then the system complexity is low, but the accuracy of finding similar patients is poor
Solution Approach 1:
The patent merges text data and image data into a unified semantic comparison space, combining information from multiple modalities to improve patient similarity search accuracy. The system integrates different data types (electronic medical records and medical images) to provide a more comprehensive basis for finding similar patients, thereby enhancing diagnostic and treatment planning capabilities
Solution Approach 2:
The semantic comparison space serves as a universal framework that can handle multiple data modalities (text and image) through the same comparison mechanism. This multi-functional approach allows the system to process diverse medical data types using a unified methodology, improving versatility without proportionally increasing complexity
Data Source
AI summary
A method and device of similarity determination, network training, and search, an electronic device, and a storage medium are provided. The data similarity determination method includes: acquiring first data of a first object; mapping the first sub-data as a first semantic representation in a semantic comparison space, where the semantic comparison space enables a similarity between a semantic representation obtained by mapping data of the first modality to the semantic comparison space and a semantic representation obtained by mapping data of the second modality to the semantic comparison space to be computed; acquiring second data of a second object; mapping the second sub-data as a second semantic representation in the semantic comparison space; and calculating a similarity between the first data and the second data based on at least the first semantic representation and the second semantic representation.