# CASCADE

**Repository Path**: wuwu-wu/CASCADE

## Basic Information

- **Project Name**: CASCADE
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-07-02
- **Last Updated**: 2025-07-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CASCADE

Official Pytorch implementation of [Medical Image Segmentation via Cascaded Attention Decoding, WACV 2023](https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html).  
[GAIN 2023 best poster award](https://sites.utexas.edu/gain/previous-winners/prev-gain-2023-posterwinners), Won The Texas Health Catalyst Award 2024.
<br>
[Md Mostafijur Rahman](https://github.com/mostafij-rahman), [Radu Marculescu](https://radum.ece.utexas.edu/)
<p>The University of Texas at Austin</p>

### 🔍 **Check out our CVPR 2024 paper! [EMCAD](https://github.com/SLDGroup/EMCAD)** 
### 🔍 **Check out our CVPRW 2024 paper! [PP-SAM](https://github.com/SLDGroup/PP-SAM)** 
### 🔍 **Check out our WACV 2024 paper! [G-CASCADE](https://github.com/SLDGroup/G-CASCADE)**
### 🔍 **Check out our MIDL 2023 paper! [MERIT](https://github.com/SLDGroup/MERIT)** 

## Architecture

<p align="center">
<img src="cascade_architecture.jpg" width=100% height=40% 
class="center">
</p>

## Usage:
### Recommended environment:
```
Python 3.8
Pytorch 1.11.0
torchvision 0.12.0
```
Please use ```pip install -r requirements.txt``` to install the dependencies.

### Data preparation:
- **Synapse Multi-organ dataset:**
Sign up in the [official Synapse website](https://www.synapse.org/#!Synapse:syn3193805/wiki/89480) and download the dataset. Then split the 'RawData' folder into 'TrainSet' (18 scans) and 'TestSet' (12 scans) following the [TransUNet's](https://github.com/Beckschen/TransUNet/blob/main/datasets/README.md) lists and put in the './data/synapse/Abdomen/RawData/' folder. Finally, preprocess using ```python ./utils/preprocess_synapse_data.py``` or download the [preprocessed data](https://drive.google.com/file/d/1tGqMx-E4QZpSg2HQbVq5W3KSTHSG0hjK/view?usp=share_link) and save in the './data/synapse/' folder. 
Note: If you use the preprocessed data from [TransUNet](https://drive.google.com/drive/folders/1ACJEoTp-uqfFJ73qS3eUObQh52nGuzCd), please make necessary changes (i.e., remove the code segment (line# 88-94) to convert groundtruth labels from 14 to 9 classes) in the utils/dataset_synapse.py. 

- **ACDC dataset:**
Download the preprocessed ACDC dataset from [Google Drive of MT-UNet](https://drive.google.com/file/d/13qYHNIWTIBzwyFgScORL2RFd002vrPF2/view) and move into './data/ACDC/' folder.

- **Polyp datasets:**
Download training and testing datasets [Google Drive](https://drive.google.com/file/d/1pFxb9NbM8mj_rlSawTlcXG1OdVGAbRQC/view?usp=sharing) and move them into './data/polyp/'.


### Pretrained model:
You should download the pretrained PVTv2 model from [Google Drive](https://drive.google.com/drive/folders/1Eu8v9vMRvt-dyCH0XSV2i77lAd62nPXV?usp=sharing), and then put it in the './pretrained_pth/pvt/' folder for initialization. 

Download Google pretrained ViT models (R50-ViT-B_16, ViT-B_16, ...) from [Google Cloud](https://console.cloud.google.com/storage/browser/vit_models/imagenet21k) or use ```wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz```, and then put them in the './pretrained_pth/vit/imagenet21k/' folder for initialization. 

### Training:
```
cd into CASCADE
```
For Polyp training run ```CUDA_VISIBLE_DEVICES=0 python -W ignore train_polyp.py``` 

For Synapse Multi-organ training run ```CUDA_VISIBLE_DEVICES=0 python -W ignore train_synapse.py```

For ACDC training run ```CUDA_VISIBLE_DEVICES=0 python -W ignore train_ACDC.py```

### Testing:
```
cd into CASCADE 
```
For Polyp testing run ```CUDA_VISIBLE_DEVICES=0 python -W ignore test_polyp.py``` 

For Synapse Multi-organ testing run ```CUDA_VISIBLE_DEVICES=0 python -W ignore test_synapse.py```

For ACDC testing run ```CUDA_VISIBLE_DEVICES=0 python -W ignore test_ACDC.py```

## Acknowledgement
We are very grateful for these excellent works [PraNet](https://github.com/DengPingFan/PraNet), [Polyp-PVT](https://github.com/DengPingFan/Polyp-PVT) and [TransUNet](https://github.com/Beckschen/TransUNet), which have provided the basis for our framework.

## Citations

``` 
@InProceedings{Rahman_2023_WACV,
    author    = {Rahman, Md Mostafijur and Marculescu, Radu},
    title     = {Medical Image Segmentation via Cascaded Attention Decoding},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2023},
    pages     = {6222-6231}
} 
```