This repo holds the code of TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

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Deep Learning TransFuse
Overview

TransFuse

This repo holds the code of TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

Requirements

  • Pytorch>=1.6.0, <1.9.0 (>=1.1.0 should work but not tested)
  • timm==0.3.2

Model Overview


Experiments

ISIC2017 Skin Lesion Segmentation Challenge

GPUs of memory>=4G shall be sufficient for this experiment.

  1. Preparing necessary data:

    • downloading ISIC2017 training, validation and testing data from the official site, put the unzipped data in ./data.
    • run process.py to preprocess all the data, which generates data_{train, val, test}.npy and mask_{train, val, test}.npy.
    • alternatively, the processed data is provided in Baidu Pan, pw:ymrh and Google Drive.

  2. Testing:

    • downloading our trained TransFuse-S from Baidu Pan, pw:xd74 or Google Drive to ./snapshots/.
    • run test_isic.py --ckpt_path='snapshots/TransFuse-19_best.pth'.

  3. Training:

    • downloading DeiT-small from DeiT repo to ./pretrained.
    • downloading resnet-34 from timm Pytorch to ./pretrained.
    • run train_isic.py; you may also want to change the default saving path or other hparams as well.

Code of other tasks will be comming soon.

Reference

Some of the codes in this repo are borrowed from:

Citation

Please consider citing us if you find this work helpful:

@article{zhang2021transfuse,
  title={TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation},
  author={Zhang, Yundong and Liu, Huiye and Hu, Qiang},
  journal={arXiv preprint arXiv:2102.08005},
  year={2021}
}

Questions

Please drop an email to [email protected]

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Comments
  • Add large models and modify download link of resnet34

    Add large models and modify download link of resnet34

    Hello, your work helps me a lot while doing my research!

    To make this code easier to use, I made these changes:

    1. Add Large scale models, TransFuse_L, TransFuse_L_384, which were mentioned in the paper.
    2. Modify the download link of resnet34 in README, because the link writen in lib/TransFuse.py was different from the one in README.
    3. Add align_corners=True to F.interpolate, in order to avoid such WARNING
    /usr/local/miniconda3/lib/python3.7/site-packages/torch/nn/functional.py:3455: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
  "See the documentation of nn.Upsample for details.".format(mode)
    opened by xyn1201 2
  • Add PyTorch version upper bound

    Add PyTorch version upper bound

    timm==0.3.2 requires torch version less than 1.9 because timm depends on torch's internal collection_abcs in torch._six:

    https://github.com/rwightman/pytorch-image-models/blob/2ed8f247154870be7acc1908fde0a7f457f67456/timm/models/layers/helpers.py#L6

    which was removed in torch 1.9:

    https://github.com/pytorch/pytorch/blob/v1.9.0/torch/_six.py

    opened by dskkato 0
Owner
Rayicer
Rayicer
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