复现2024年CVPR图像分割SED_Simple Encoder-Decoder_Seg,并使用自己的数据集进行训练

部署运行你感兴趣的模型镜像

一.复现步骤
1.环境安装
2.数据集下载+处理
3.根据自身设备配置config和执行指令

运行结果图如下(训练时间较长,慎重考虑):

二.使用自己的数据集

1.使用labelme打标签

2.转换为模型适用数据集

3.自行创建py文件获取,获取标签颜色

4.修改预处理文件等

5.执行结果(只训练了500轮)

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### 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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