Image Segmentation Scores for Capsule Endoscopy Classification
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Solution Overview
Problem
Existing image classification systems face challenges in accurately classifying medical images, particularly those captured by capsule endoscopy, where objects of interest occupy a small fraction of the image and have similar color attributes to the surrounding tissue, leading to inconsistent and inaccurate classification.
Innovation Solution
Transform image segmentation scores into classification scores using non-machine learning and machine learning methods, such as determining maximum, average, or count of scores, identifying clusters, and analyzing pixel distributions to classify entire images based on per-pixel information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image classification systems are used, then the system complexity is low, but the classification accuracy is insufficient for medical images where objects of interest occupy a small fraction of the image
Solution Approach 1:
The patent applies segmentation by dividing the image classification task into per-pixel segmentation scores first, then aggregating these scores to determine overall image classification. This approach improves accuracy for small objects by analyzing each pixel individually rather than treating the entire image as a single unit, while the segmentation framework provides a structured method to manage the increased complexity.
Solution Approach 2:
The patent transitions from traditional holistic image classification to a multi-dimensional approach by introducing per-pixel segmentation scores as an intermediate representation. This dimensional transformation allows the system to capture fine-grained information about small objects while providing multiple aggregation strategies (max, mean, weighted mean) to balance accuracy and computational complexity.
2Measurement precision
If per-pixel segmentation analysis is performed, then classification accuracy is improved, but computational resources increase
Solution Approach 1:
The patent implements partial action by selectively applying different aggregation strategies based on the specific medical imaging task and object characteristics. Rather than always performing full per-pixel analysis with complex aggregation, the system can use simpler methods (like max pooling) when sufficient, and more sophisticated methods (like weighted mean with anatomical priors) only when needed, thus balancing computational cost with accuracy requirements.
Solution Approach 2:
The patent changes parameters by introducing configurable aggregation functions and thresholds that can be adjusted based on the specific medical application. This allows optimization of computational resources by tuning parameters such as the aggregation method (max, mean, weighted mean), threshold values for positive classification, and weighting schemes based on anatomical importance, thereby reducing unnecessary computational overhead while maintaining high accuracy.
3Productivity
If simple aggregation methods are used, then computational speed is high, but classification reliability is reduced
Solution Approach 1:
The patent applies dynamics by making the aggregation method adaptive rather than static. The system can dynamically select between different aggregation strategies (max, mean, weighted mean) and adjust threshold values based on the specific image characteristics, anatomical region, and clinical context. This dynamic approach allows the system to maintain high processing speed for routine cases while improving reliability for challenging cases that require more sophisticated analysis.
Solution Approach 2:
The patent incorporates feedback mechanisms where classification results can be reviewed and adjusted by clinicians, and this feedback is used to refine the aggregation parameters and thresholds. This feedback loop improves reliability over time by learning from actual clinical cases and adjusting the aggregation strategy accordingly, while maintaining efficient processing through the established framework.
Data Source
AI summary
The present disclosure relates to classifying and/or selecting images based on image segmentation. A classification system for classifying images includes one or more processors and at least one memory storing machine executable instructions. When the instructions are executed by the one or more processors, they cause the classification system to: access image segmentation scores for pixels of an image, and classify the entire image based on the image segmentation scores for the pixels of the image. The image segmentation scores for the pixels of the image are provided by an image segmentation system based on the image, and each of the image segmentation scores correspond to at least one pixel of the pixels of the image.