Two-Stage Medical Image Detector for Visual Finding Identification
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Solution Overview
Problem
Current machine learning models face challenges in accurately identifying visual findings, such as breast cancer, in medical images due to the difficulty in distinguishing clinically significant features from background anatomical patterns and artifacts, leading to high false positives and false negatives.
Innovation Solution
A two-stage detector system comprising a detector component and a patch classifier, where the detector generates multiple boxes with associated scores, and each box is converted into a patch fed into a convolutional neural network (CNN) patch classifier, with a dot product computed between box and patch scores to provide an image-level indication of the likelihood of a visual finding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single-stage detector is used to identify visual findings in medical images, then the system complexity is low, but the accuracy and reliability of identification are insufficient due to high false positives and false negatives
Solution Approach 1:
The detection system is segmented into two distinct stages: a detector component that generates initial boxes and scores, and a patch classifier that refines these predictions. This segmentation allows each component to specialize in specific tasks, with the detector focusing on generating candidate regions and the patch classifier focusing on accurate classification, thereby improving overall reliability without requiring a single complex monolithic model
Solution Approach 2:
A patch classifier component is introduced as an intermediary between the detector and the final identification decision. This intermediary component receives boxes from the detector, processes them through a convolutional neural network, and outputs refined scores that are combined with the original box scores via dot product. This intermediary stage acts as a mediator that filters false positives and refines detections, significantly improving reliability
2Reliability
If the detector generates multiple boxes with scores and processes them through patch classification, then the sensitivity and specificity of visual finding identification increase, but the computational time and processing speed decrease
Solution Approach 1:
The detector component performs preliminary action by generating multiple boxes with initial scores before the patch classification stage. This preliminary detection creates a set of candidate regions that can be processed in parallel by the patch classifier, allowing the system to maintain high sensitivity and specificity while managing computational load through structured processing stages
Solution Approach 2:
The system generates more boxes than strictly necessary (excessive action) to ensure comprehensive coverage of potential visual findings. By processing multiple candidate boxes through the patch classifier, the system maintains high sensitivity even though it increases computational requirements. The dot product combination of scores provides a mechanism to manage this excess by weighting the contributions of multiple detections
3Object-affected harmful factors
If a two-stage detector with dot product computation is implemented, then false positives are reduced and only potentially cancerous findings are flagged, but the device complexity and training data requirements increase
Solution Approach 1:
The patch classifier receives feedback from the detector's box scores and uses this information to refine its classification. The dot product computation combines the original box scores with the patch classifier scores, creating a feedback loop where the final identification decision incorporates information from both stages. This feedback mechanism effectively filters false positives by requiring consensus between the detector and patch classifier
Solution Approach 2:
The system creates a composite detection approach by combining two different types of neural network components: a detector component that generates boxes and a patch classifier that performs classification. This composite architecture leverages the strengths of both components - the detector's ability to generate candidate regions and the patch classifier's ability to accurately distinguish between benign and malignant findings - thereby reducing false positives
Data Source
AI summary
There is provided a method, comprising feeding a medical image into a detector component trained on a first training dataset of medical images annotated with ground truth boxes depicting a visual finding, obtaining boxes each associated with a respective box score indicative of likelihood of the visual finding, converting each respective box into a respective patch, feeding patches into a patch classifier trained on a second training dataset that includes patches extracted from the ground truth box labels of the first training dataset, wherein a patch score for a patch corresponds to a box score obtained from a box corresponding to the patch, obtaining patch scores indicative of likelihood of the visual finding being depicted, and computing a dot product of the box scores and the patch scores, and providing the dot product as an image-level indication of likelihood of the visual finding being depicted in the medical image.