Detection Score Map Quality Screening for Segmentation Output
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deep learning-based detection and segmentation algorithms often fail to produce acceptable results when analyzing data far from their training dataset distribution, leading to reduced user confidence in the tools utilizing these algorithms.
Innovation Solution
A system and method that automatically detects the quality of detection and segmentation algorithm outputs by analyzing intermediate outputs at multiple operating points, using a detection score map to compute features, and applying a classifier to predict whether the final output meets a detection precision threshold, preventing poor-quality outputs from being shown to end users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning-based detection and segmentation algorithms are applied to data far from training dataset distribution, then the algorithms can process diverse inputs, but the output quality deteriorates and becomes unreliable
Solution Approach 1:
The system performs preliminary quality assessment of algorithm outputs before presenting them to users. By computing features from detection score maps and using a trained classifier to predict quality metrics, the system proactively identifies and filters poor-quality outputs before they reach the end user, preventing unreliable results from being displayed.
Solution Approach 2:
The system implements a feedback mechanism where the quality assessment results are fed back into the workflow to control output display. The classifier's predictions about detection precision and other quality metrics provide feedback that determines whether algorithm outputs should be shown to users, creating a closed-loop quality control system.
2Reliability
If algorithm outputs are automatically quality-assessed before display, then user confidence and reliability improve, but processing time and system complexity increase
Solution Approach 1:
The quality assessment system is segmented into distinct functional components: feature computation from detection score maps, classification model inference for quality prediction, and decision logic for output filtering. This modular segmentation allows each component to be optimized independently and integrated into existing workflows without requiring complete system redesign.
3Manufacturing precision
If quality assessment is performed on all algorithm outputs, then only high-quality results are displayed, but processing time increases
Solution Approach 1:
The system performs quality assessment at multiple operating points by computing features from detection score maps and evaluating multiple quality metrics (detection precision, false positive rate, recall) rather than a single metric. This partial assessment approach focuses computational resources on the most critical quality dimensions, achieving sufficient quality control without exhaustive analysis of all possible parameters.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
In an approach for automatically detecting whether an output of a detection and segmentation algorithm is of an acceptable quality, a processor receives an image. A processor applies a detection stage of a detection and segmentation algorithm to the image. A processor computes a set of features from a detection score map output by the detection stage of the detection and segmentation algorithm by analyzing the detection score map at more than one different operating points. A processor inputs the set of features into a classifier that predicts whether a final output of the detection and segmentation algorithm will be of an acceptable quality, wherein the acceptable quality is defined based on whether a detection precision threshold has been reached. A processor receives an output of the classifier.