Unified Image Model for Missing Modality Segmentation
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Solution Overview
Problem
Current methods for processing multimodal images with missing modalities are complex and reduce accuracy due to the need for intricate model designs and increased parameters, leading to suboptimal performance in segmentation tasks.
Innovation Solution
A training method that involves reconstructing full-modality images using initialized models, performing consistency loss-based updates, and employing self-distillation for improved segmentation accuracy across various modalities, allowing a single model to handle all missing-modality situations efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex model designs are used to process multimodal images with missing modalities, then the model can handle the complexity of missing modalities, but processing procedures become complicated, more parameters and calculations are needed for training and deployment, and accuracy is reduced
Solution Approach 1:
The patent applies universality by designing a single unified model that can handle both full-modality and missing-modality images. The model uses a shared backbone network for feature extraction that works across different modality conditions, eliminating the need for separate complex models for each scenario. This unified approach maintains adaptability to missing modalities while reducing overall model complexity.
Solution Approach 2:
The patent segments the processing into distinct stages: feature extraction using a shared backbone, modality-specific feature enhancement, and segmentation output. This segmentation allows the model to handle missing modalities efficiently by processing only the available modalities through the appropriate pathways, reducing unnecessary computations and parameters while maintaining accuracy.
2Reliability
If more parameters and calculations are used for training and deployment, then the model can process complex multimodal images, but processing procedures become complicated and accuracy of segmenting the multimodal image is reduced
Solution Approach 1:
The patent applies preliminary action by performing feature extraction and modality-specific enhancement before the final segmentation task. The shared backbone network pre-processes all input modalities to extract common features, and modality-specific branches pre-enhance unique characteristics. This preliminary processing organizes the data efficiently, reducing the computational burden during deployment while maintaining high segmentation accuracy.
Solution Approach 2:
The patent merges the feature extraction processes into a single shared backbone network that processes all modalities simultaneously. This merging eliminates redundant computations that would occur if separate networks were used for each modality, reducing training and deployment complexity while maintaining the ability to capture both common and modality-specific features for accurate segmentation.
3Device complexity
If a single model is used to handle all missing-modality situations, then computational complexity and memory requirements are reduced, but the model must efficiently learn implicit relationships between multiple modalities
Solution Approach 1:
The patent introduces an intermediary mechanism in the form of modality-specific feature enhancement branches that connect the shared backbone to the segmentation head. These intermediary branches learn and model the relationships between different modalities by enhancing features specific to each modality while relying on the common features from the backbone. This intermediary structure simplifies the computational complexity of the unified model while effectively capturing the implicit relationships between modalities.
Solution Approach 2:
The patent uses parameter changes dynamically based on the input modality configuration. The model adjusts which feature enhancement branches are activated depending on which modalities are present, changing the effective parameters in use without requiring separate models for each scenario. This approach reduces computational complexity by only processing available modalities while still learning the relationships between all possible modalities during training.
Data Source
AI summary
This application provides a training method and apparatus for an image processing model, an electronic device, and a storage medium. The method includes: obtaining a plurality of multimodal images used as training samples, types of the multimodal images including full-modality images and missing-modality images; invoking, based on each of the multimodal images, an initialized image processing model to execute a first training task for reconstructing the full-modality image, the image processing model outputting a first full-modality reconstructed image in a process of executing the first training task; performing image completion processing on each of first full-modality reconstructed images based on the full-modality image, to obtain a full-modality template image; determining a consistency loss between a multimodal image pair and the full-modality template image; and invoking, based on each of the multimodal images, a trained image processing model to execute a second training task for segmenting each of the multimodal images, and using the consistency loss as a constraint condition in the second training task.