Medical Image Retrieval Using Multi-Modal Feature Vectors
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Solution Overview
Problem
Current methods for finding similar patients based on medical images, particularly in cancer diagnosis, are labor-intensive, time-consuming, and prone to variability, as they rely on manual examination and content-based image retrieval systems that fail to effectively match local patterns and incorporate non-imaging data.
Innovation Solution
A computer-implemented method using a machine learning model trained on pairs of medical images and non-imaging data to determine feature vectors, allowing for the identification of similar medical images by comparing these vectors, thereby incorporating both visual and non-visual patient data for more accurate matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination of histopathology slides is used to identify and compare tumor patterns, then diagnostic accuracy can be maintained through expert review, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical examination process with an automated machine learning system. The ML model automatically analyzes histopathology slides to generate feature vectors, eliminating the need for manual pathologist review while maintaining diagnostic accuracy. This substitution of mechanical human examination with automated computational analysis resolves the contradiction between maintaining precision and improving productivity.
Solution Approach 2:
The system enables self-service by allowing the computer system to autonomously perform the diagnostic comparison task without requiring expert pathologist intervention. The ML model independently processes images, generates feature vectors, and identifies similar cases, making the system self-sufficient while improving processing efficiency.
2Measurement precision
If content-based image retrieval systems use global features like color, texture, and shape to find analogous images, then visual similarity can be achieved, but local patterns and non-imaging data are not effectively incorporated
Solution Approach 1:
The patent extends the retrieval system from a single-dimensional visual feature space to a multi-dimensional feature space that incorporates both imaging and non-imaging data. The ML model generates feature vectors that encode information from histopathology images, genomic data, and clinical parameters, creating a comprehensive multi-dimensional representation that preserves both visual similarity and non-imaging information.
Solution Approach 2:
The system merges previously separate data sources (visual image features and non-visual clinical/genomic data) into a unified feature vector representation. By combining these diverse data types through the ML model, the system achieves both visual similarity matching and retention of non-imaging information, resolving the information loss problem.
3Measurement precision
If feature vectors are generated by applying a machine learning model trained on training datasets to medical images, then accurate matching of both visual and non-visual characteristics is achieved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-training the machine learning model on comprehensive training datasets that include both imaging and non-imaging data. This pre-training phase extracts and encodes relevant features into compact feature vectors, so that during actual retrieval operations, the system only needs to perform efficient vector comparisons rather than complex multi-modal analysis, thereby reducing operational computational complexity while maintaining high matching accuracy.
Data Source
AI summary
A method for providing a similar medical image comprises: receiving a first medical image related to a first patient; determining a first feature vector by applying a first machine learning model to the first medical image, the first machine learning model having been trained based on training datasets, and each of the training datasets including a training medical image and related non-imaging data; receiving a plurality of second feature vectors; determining, based on the first feature vector, a similar feature vector from the plurality of second feature vectors; selecting the similar medical image from the plurality of second medical images, wherein the similar medical image is related to the similar feature vector; and providing the similar medical image.