Confidence-Based Modality Selection for CAD Diagnosis
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Solution Overview
Problem
The increased workload for radiologists due to the need for regular low-dose CT screenings for early cancer detection, particularly lung cancer, is not efficiently managed by existing CAD systems, which often require multiple tests with varying usefulness, leading to medical, practical, and financial limitations.
Innovation Solution
A method and platform that classify regions of interest in imaging data by calculating feature vectors, projecting them using a decision function based on multiple modalities, and estimating confidence to determine the necessity of additional tests, thereby optimizing the use of resources and reducing unnecessary testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple tests with different modalities are performed to improve classification accuracy, then diagnosis reliability is improved, but loss of time and financial cost increase
Solution Approach 1:
The system performs preliminary analysis using available imaging data to estimate the confidence of classification before committing to additional tests. By calculating feature vectors and using decision functions to assess current diagnostic confidence, the system determines in advance whether further testing is necessary, thereby avoiding unnecessary time loss while maintaining high classification accuracy when needed
Solution Approach 2:
The system applies partial action by selectively performing only the necessary subset of available tests rather than all possible modalities. The confidence estimation mechanism allows the system to achieve sufficient classification accuracy with minimal required tests, avoiding the excessive time and resource consumption of comprehensive multi-modality testing while maintaining diagnostic reliability
2Reliability
If multiple tests with different modalities are performed to improve classification accuracy, then diagnosis reliability is improved, but financial cost increases
Solution Approach 1:
The system performs preliminary confidence estimation using available imaging data and feature vectors before authorizing additional expensive tests. By assessing whether current data suffices for reliable classification, the system avoids unnecessary financial expenditure on redundant modalities while ensuring that essential tests are performed to achieve adequate diagnostic accuracy
Solution Approach 2:
The system implements partial action by performing only the minimal necessary tests to achieve sufficient classification confidence. Rather than routinely ordering all available modalities, the confidence estimation mechanism enables selective testing that reduces financial cost while maintaining the reliability needed for accurate cancer diagnosis
3Reliability
If more tests are performed to improve classification reliability, then diagnosis quality improves, but device and resource complexity increases
Solution Approach 1:
The confidence estimation mechanism serves multiple functions: it assesses classification reliability, determines the need for additional testing, and guides resource allocation. This multi-functional approach consolidates what would otherwise require separate complex systems for each function, thereby improving diagnosis quality while managing overall system complexity through a unified decision-making framework
Data Source
AI summary
The invention provides a method and apparatus for classifying a region of interest in imaging data, the method comprising:calculating a feature vector for at least one region of interest in the imaging data, said feature vector including features of a first modality;projecting the feature vector for the at least one region of interest in the imaging data using a decision function to generate a classification, wherein the decision function is based on classified feature vectors including features of a first modality and features of a second modality;estimating the confidence of the classification if the feature vector is enhanced with features of the second modality.