Image-Text Deep Neural Network for Patch-Wise CXR Pathology Detection
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Solution Overview
Problem
Current AI algorithms for CXR abnormality analysis provide limited accuracy and reliability, especially for pathologies like pneumothorax, and lack spatial information that aids medical practitioners in understanding and validating the AI model's output.
Innovation Solution
An image-text deep neural network (DNN) is employed to process medical imaging data, using a vision transformer architecture for patch-wise prediction, which integrates a text prompt to provide localized findings of pathologies, enabling unsupervised learning and refining predictions through a patch-wise loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If AI algorithms provide classification results for the overall CXR image, then the analysis covers the entire observation region, but the output lacks spatial information making it difficult for medical practitioners to assess relevance and reliability
Solution Approach 1:
The CXR image is divided into multiple spatial patches, and the AI algorithm generates separate classification results for each patch. This segmentation approach preserves spatial information by providing localized predictions while maintaining the overall coverage of the image, allowing medical practitioners to assess relevance and reliability of findings in specific regions.
2Reliability
If reference AI algorithms are used for CXR abnormality analysis, then the analysis can be performed, but the accuracy is limited especially for pathologies like pneumothorax with varying manifestations
Solution Approach 1:
The algorithm applies local quality by generating specialized classification results for each spatial patch rather than applying a uniform classification to the entire image. This allows the system to adapt to varying manifestations of pathologies like pneumothorax by providing localized predictions that capture region-specific characteristics, thereby improving detection accuracy for diverse pathology presentations.
Data Source
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AI summary
Imaging data is processed in an image-text deep neural network, e.g., a vision transformer deep neural network. The image-text deep neural network also processes a text input indicative of a pathology. For each of multiple spatial patches within an observation region, a respective prediction of the presence or the absence of a finding of a pathology is provided.