Multiple Medical Image Interpretation with Paired ROI Deep Learning
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Solution Overview
Problem
Existing medical image interpretation systems struggle to balance global and local context in cancer diagnosis, often failing to provide adequate pixel-level attribution and broader anatomical context, leading to inefficiencies in radiological analysis.
Innovation Solution
A method utilizing deep learning models, including deep convolutional neural networks and self-attention mechanisms, processes paired medical images by identifying regions of interest and their corresponding reference regions across multiple modalities or time points, generating probabilistic predictions, and providing visualizations to aid human diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models process paired medical images with regions of interest and reference regions, then diagnostic accuracy is improved through integrated global and local context, but computational resource requirements and processing time increase
Solution Approach 1:
The patent segments the medical image analysis task into distinct components: identifying regions of interest (ROIs), locating corresponding reference regions in paired images, extracting features from both regions, and integrating them through a deep learning model. This segmentation allows the system to process only relevant portions of images rather than entire images, reducing computational burden while maintaining diagnostic accuracy.
Solution Approach 2:
The patent extracts and focuses on specific regions of interest and their corresponding reference regions from the full medical images. By taking out only the necessary portions (ROIs and reference regions) for analysis rather than processing complete images, the system reduces computational resource requirements while preserving the critical diagnostic information needed for accurate cancer detection.
2Measurement precision
If the system processes multiple paired images and generates detailed visualizations with pixel-level attribution, then diagnostic accuracy and explainability are improved, but processing time and system complexity increase
Solution Approach 1:
The patent segments the image processing workflow into sequential steps: ROI identification, reference region localization, feature extraction, prediction generation, and visualization. This segmentation allows each step to be optimized independently and enables the system to process images efficiently by focusing computational efforts only on relevant regions rather than analyzing every pixel of entire images.
Solution Approach 2:
The patent applies partial action by analyzing only the necessary portions of images (ROIs and reference regions) rather than processing complete images. This selective processing approach reduces overall processing time while maintaining diagnostic accuracy, as the system extracts and analyzes only the critical information needed for cancer detection and characterization.
3Reliability
If the deep learning model integrates features from multiple imaging modalities and time points, then comprehensive cancer detection is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent creates a universal framework that can handle multiple imaging modalities (mammography, CT, MRI, ultrasound) and temporal relationships (current images, prior images, contralateral images) through a single integrated deep learning model. The model is designed to process paired images from different modalities and time points using the same architectural components, reducing overall system complexity while improving cancer detection reliability through comprehensive multi-source analysis.
Solution Approach 2:
The patent merges the processing of multiple imaging modalities and temporal data into a unified deep learning framework. By combining features from different image types (mammograms, CT scans, MRIs) and time points within a single model architecture, the system achieves comprehensive cancer detection without requiring separate specialized systems for each modality, thereby reducing overall device complexity.
Data Source
AI summary
A method is disclosed of processing a set of images. Each image in the set has an associated counterpart image. One or more regions of interest (ROIs) are identified in one or more of the images in the set of images. For ROI identified, a reference region is identified in the associated counterpart image. ROIs and associated reference regions are cropped out, thereby forming cropped pairs of images 1 . . . n1, that are fed to a deep learning model trained to make a prediction of probability of a state of the ROI, e.g., disease state, which generates a prediction Pi-, (i=1 . . . n) for each cropped pair. The model generates an overall prediction P from each of the predictions Pi. A visualization of the set of medical images and the associated counterpart images including the cropped pair of images is generated.