Medical Image ROI Pairing With Attention for Cancer Diagnosis
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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 workflows.
Innovation Solution
A deep learning-based method that identifies regions of interest across multiple medical images, aligns and crops them with counterpart images, and uses a self-attention mechanism to generate probabilistic predictions, incorporating global context through feature extraction and concatenation, followed by visualization to aid human diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models process entire medical images to provide global context, then diagnostic accuracy improves, but computational time and resource requirements increase
Solution Approach 1:
The patent divides the medical image into multiple patches and processes them in parallel using multiple deep learning models. Each model processes a specific patch independently, allowing concurrent computation that reduces overall processing time while maintaining the ability to capture global context through attention mechanisms that aggregate information across all patches.
2Measurement precision
If deep learning models focus on local regions to improve detection precision, then pixel-level attribution improves, but broader anatomical context is lost
Solution Approach 1:
The patent introduces attention mechanisms as intermediaries between local patch processing and global context integration. The attention mechanism weights and aggregates features from multiple local patches, allowing the model to focus on relevant regions while maintaining awareness of the broader anatomical context through the weighted combination of all patch features.
3Reliability
If multiple images are processed simultaneously to improve diagnostic reliability, then cancer detection accuracy improves, but processing complexity increases
Solution Approach 1:
The patent segments the processing task by assigning different deep learning models to different image patches, allowing parallel processing of multiple images. This segmentation approach maintains diagnostic reliability by processing all images while organizing the complexity into manageable, independent model instances that can operate concurrently.
Data Source
AI summary
A method is disclosed of processing a set of images. Each image in the set has an associated counterpart image, e.g., a contralateral, prior or multimodal 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. The cropped image pairs 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 for each cropped pair. The model generates an overall prediction P from each of the predictions. A visualization of the set of medical images and the associated counterpart images including the cropped pair of images is generated, e.g., on a workstation.