5# Medical-image-model 这个项目包含一些常用且经典的网络架构整理,读研后期的才想起来整理,后期会不定时的更新。
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整理这些网络架构有什么用?
这些经典网络架构可以拿来做对比实验,也可以在网络的基础上进行修改缝合创新。
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为什么不提供其他代码,例如预处理、模型训练、测试、评估等代码?
每个人都应有自己的一个训练框架,你可以使用nnUNet的框架,也可以使用你任何熟悉的框架进行训练。
上传了医学图像分割领域的经典分割框架TransUNet, 该项目代码可以直接使用,与原论文不一致的是:没有采用预训练的Vit进行初始化。仅复现了该网络的框架。可以在Config里面修改自己的配置。
If you find 3D-TransUNet useful for your research and applications, please cite using this BibTeX:
@article{chen2023transunet3d,
title={3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers},
author={Chen, Jieneng and Mei, Jieru and Li, Xianhang and Lu, Yongyi and Yu, Qihang and Wei, Qingyue},
journal={arXiv preprint arXiv:2310.07781},
year={2023}
}
上传了TransBTS,该网络架构用于Brats脑肿瘤数据集的3D分割。可以在model = BTS()
里面修改自己想要的参数.
上传了AttUnet,该网络是经典的医学图像分割框架。
If you find TransBTS useful for your research and applications, please cite using this BibTeX:
@inproceedings{wang2021transbts,
title={TransBTS: Multimodal Brain Tumor Segmentation Using Transformer},
author={Wang, Wenxuan and Chen, Chen and Ding, Meng and Yu, Hong and Zha, Sen and Li, Jiangyun},
booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part I 24},
pages={109--119},
year={2021},
organization={Springer}
}
@article{oktay2018attention,
title={Attention u-net: Learning where to look for the pancreas},
author={Oktay, Ozan and Schlemper, Jo and Folgoc, Loic Le and Lee, Matthew and Heinrich, Mattias and Misawa, Kazunari and Mori, Kensaku and McDonagh, Steven and Hammerla, Nils Y and Kainz, Bernhard and others},
journal={arXiv preprint arXiv:1804.03999},
year={2018}
}
上传了SegMamba,运行该代码时请先装好pytorch
,建议重新创建一个环境,然后按照官方的安装步骤安装依赖即可。
@article{xing2024segmamba,
title={Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation},
author={Xing, Zhaohu and Ye, Tian and Yang, Yijun and Liu, Guang and Zhu, Lei},
journal={arXiv preprint arXiv:2401.13560},
year={2024}
}