Universal Multi-Modal Deep Network for Medical Image Analysis
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Solution Overview
Problem
Current approaches to medical image analysis require multiple deep learning networks for each modality and viewpoint, leading to scalability issues and the need for a large number of network parameters, making it inefficient to train and deploy in radiology settings where data collection and annotation are costly and regulated.
Innovation Solution
A universal multi-modal deep network architecture based on the U-net design with two output heads for segmentation and classification, allowing for simultaneous training across different modalities and viewpoints, reducing the need for multiple networks and enabling efficient training with fewer examples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple deep learning networks are used for each modality and viewpoint, then classification and segmentation accuracy can be maintained, but device complexity and memory usage increase significantly
Solution Approach 1:
The patent combines multiple separate deep learning networks (one for each modality and viewpoint) into a single unified network. This unified network processes all modalities and viewpoints simultaneously, reducing the total number of networks while maintaining classification and segmentation accuracy through shared feature extraction layers and modality-specific output layers.
Solution Approach 2:
The unified deep learning network is designed to handle multiple modalities (CT, MRI, PET) and multiple viewpoints (axial, coronal, sagittal) within a single architecture. The network uses shared convolutional layers for feature extraction that work across all modalities, while incorporating modality-specific and viewpoint-specific branches that maintain specialized processing capabilities.
2Reliability
If multiple deep learning networks are used for each modality and viewpoint, then task-specific performance can be optimized, but training efficiency decreases due to large number of parameters
Solution Approach 1:
By merging multiple task-specific networks into one unified network, the patent reduces the total number of trainable parameters. The shared feature extraction layers are trained once on combined data from all modalities and viewpoints, eliminating redundant parameter learning and significantly improving training efficiency while maintaining task-specific performance through specialized output branches.
Solution Approach 2:
The unified network's multi-functional architecture allows simultaneous training on diverse medical imaging data (CT, MRI, PET across multiple viewpoints). This shared training process leverages transfer learning and feature reuse across modalities, reducing the data and computational resources needed for training compared to separate specialized networks.
3Reliability
If separate networks are deployed for each modality and viewpoint, then inference can be task-optimized, but inference time increases due to multiple network executions
Solution Approach 1:
The patent merges multiple inference processes into a single unified network execution. The network processes all modalities and viewpoints simultaneously through shared computational layers, producing classification and segmentation results for all tasks in one forward pass, thereby reducing total inference time while maintaining task-optimized performance through specialized output branches.
Solution Approach 2:
The unified network's multi-functional design enables it to perform classification and segmentation across all modalities and viewpoints within a single inference pass. The architecture efficiently routes input data through shared feature extraction to modality-specific and viewpoint-specific processing branches, delivering optimized task performance without requiring multiple separate network executions.
Data Source
AI summary
A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a multi-modal classification and segmentation engine for anatomical segmentation identifying modes and viewpoints in biomedical images. The mechanism trains a neural network perform simultaneous classification and segmentation using a set of training images. The neural network provides a classification output that identifies a class label and a second output that identifies a segmentation label. The multi-modal classification and segmentation engine provides a biomedical image as the input image to the neural network. The neural network outputs a plurality of class label probabilities for a plurality of class labels and a plurality of segmentation label probabilities for each of a plurality of segmentation labels. A post-processing component executing within the multi-modal classification and segmentation engine classifies the biomedical image as an identified modality and an identified viewpoint based on the plurality of class label probabilities. The multi-modal classification and segmentation engine segments the biomedical image based on the plurality of segmentation label probabilities. The multi-modal classification and segmentation engine outputs the classified and segmented biomedical image.