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https://github.com/zylbuaa/TFormer
The official implementation of "TFormer: A throughout fusion transformer for multi-modal skin lesion diagnosis"
https://github.com/zylbuaa/TFormer
medical-image-analysis multi-modal-fusion skin-lesion-classification transformer
Last synced: 7 days ago
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The official implementation of "TFormer: A throughout fusion transformer for multi-modal skin lesion diagnosis"
- Host: GitHub
- URL: https://github.com/zylbuaa/TFormer
- Owner: zylbuaa
- License: mit
- Created: 2023-01-27T10:06:50.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-29T11:59:17.000Z (10 months ago)
- Last Synced: 2024-08-02T15:32:34.528Z (3 months ago)
- Topics: medical-image-analysis, multi-modal-fusion, skin-lesion-classification, transformer
- Language: Python
- Homepage:
- Size: 521 KB
- Stars: 12
- Watchers: 2
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TFormer
[![MIT Licence](https://badges.frapsoft.com/os/mit/mit.svg?v=103)](https://opensource.org/licenses/mit-license.php)## Introduction
The official implementation of "TFormer: A throughout fusion transformer for multi-modal skin lesion diagnosis"![Our Network Structure](graphical_abstract.png)
## Enviroments
- Windows/Linux both support
- python 3.9
- PyTorch 1.12.1
- torchvision## Prepare dataset
Please at first download datasets [Derm7pt](https://derm.cs.sfu.ca/Download.html) and then download the pretrained model of swin-tiny on ImageNet-1k from [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth). Save the model into the folder "./models/swin_transformer".## Run details
To train our `TFormer`, run:
```
python train.py --dir_release "your dataset path" --epochs 100 --batch_size 32 --learning_rate 1e-4 --cuda True
```## License
This project is licensed under the MIT License. See [LICENSE](LICENSE) for details## Acknowledgement
Our code borrows a lot from:
- [Swin-Transformer](https://github.com/microsoft/Swin-Transformer)
- [Derm7pt](https://github.com/jeremykawahara/derm7pt)