首页[CVPR 2024]MedM2G_Unifying_Medical_Multi-Modal_Generation_via_Cross-Guided_Diffusion_with_Visual_CVPR_2024_paper

[CVPR 2024]MedM2G_Unifying_Medical_Multi-Modal_Generation_via_Cross-Guided_Diffusion_with_Visual_CVPR_2024_paper

时间: 2025-06-23 17:27:42 浏览: 16

### CVPR 2024 MedM2G Paper Overview The paper titled "MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant" introduces a novel approach to unify medical multi-modal generation using cross-guided diffusion models that incorporate visual invariants[^1]. This method aims at improving the consistency and quality of generated medical data across different modalities, which is crucial for various applications such as diagnosis support systems. #### Key Contributions - **Cross-Guided Diffusion Framework**: A framework designed specifically for generating high-quality medical images by leveraging information from multiple sources or views. - **Visual Invariance Integration**: Ensures that the generative model can produce results invariant under certain transformations while preserving key anatomical features essential for clinical interpretation. - **Unified Approach**: Proposes an integrated solution capable of handling diverse types of medical imaging tasks within one cohesive system rather than requiring separate models per task. #### Methodology To achieve these goals, MedM2G employs advanced techniques including but not limited to: - Utilizing pre-trained networks pretrained on large-scale datasets like ImageNet for feature extraction purposes before feeding into the main architecture. - Implementing conditional variational autoencoders (cVAEs) alongside GANs to ensure both diversity and fidelity in synthesized outputs. ```python import torch.nn as nn class MedM2G(nn.Module): def __init__(self): super(MedM2G, self).__init__() # Define layers here based on cVAE and GAN architectures def forward(self, x): # Forward pass implementation goes here return output ```

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