Multi-modal Medical Image Encoder for Single-modality ROI Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer-aided diagnosis systems for medical images lack reliability and often require additional data due to limitations in image resolution and clarity, necessitating manual analysis by medical professionals.
Innovation Solution
A method for automatically identifying regions of interest in medical images using a trained encoder and classifier based on multimodal data inputs, which generates a joint representation without relying on hand-engineered features, allowing for improved accuracy in identifying regions of interest even with single-modality input during runtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computer-aided diagnosis systems use single-modality image data, then the processing speed is fast, but the diagnostic accuracy and reliability are insufficient
Solution Approach 1:
The system performs preliminary encoding and feature extraction for multiple modalities during the training phase, creating pre-trained encoder components. During runtime, when only single-modality data is available, these pre-trained components can be directly applied without requiring all modalities to be present, thus improving reliability while maintaining operational simplicity
Solution Approach 2:
The encoder architecture is designed to be universal across multiple modalities (X-ray, MRI, CT, ultrasound, etc.). The same encoder structure can process different types of medical imaging data by adjusting only the input layer, allowing the system to achieve multi-modality diagnostic accuracy while maintaining a relatively simple single-modality operational mode
2Productivity
If medical professionals manually analyze all image data, then the diagnostic accuracy is high, but the time consumption and workload are excessive
Solution Approach 1:
The system introduces an intermediary computer-aided diagnosis layer between the raw medical images and the final clinical decision. This intermediary uses trained encoders and classifiers to automatically identify and highlight regions of interest, providing a preliminary analysis that guides manual review and improves overall diagnostic efficiency without sacrificing accuracy
Solution Approach 2:
The diagnostic process is segmented into automatic region identification (performed by the trained system) and manual verification (performed by medical professionals). The system segments the large-scale image analysis task from the final diagnostic decision-making, allowing high-speed automated processing of routine identification while preserving human judgment for complex cases
3Reliability
If additional data and tests are performed to confirm preliminary diagnosis, then the diagnostic reliability is improved, but the loss of time and increased cost occur
Solution Approach 1:
The system provides feedback in the form of confidence scores and region-of-interest highlights for preliminary diagnoses. When the system's confidence is high and the findings are clear, no additional tests are needed. When confidence is low or findings are ambiguous, the system automatically flags cases requiring further investigation, optimizing the use of additional testing resources and reducing unnecessary delays
Data Source
AI summary
The present invention relates to the identification of regions of interest in medical images. More particularly, the present invention relates to the identification of regions of interest in medical images based on encoding and/or classification methods trained on multiple types of medical imaging data.Aspects and/or embodiments seek to provide a method for training an encoder and/or classifier based on multimodal data inputs in order to classify regions of interest in medical images based on a single modality of data input source.