# cDAL
**Repository Path**: yoveria/cDAL
## Basic Information
- **Project Name**: cDAL
- **Description**: Conditional diffusion model with spatial attention and latent embedding for medical image segmentation
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-04-28
- **Last Updated**: 2026-04-28
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# PyTorch implementation of "Conditional diffusion model with spatial attention and latent embedding" [MICCAI 2024] #
Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention and latent embedding
(cDAL) for medical image segmentation. In cDAL, a convolutional neural network (CNN) based discriminator is used at every time-step of the diffusion process to distinguish between the generated labels and the real
ones. A spatial attention map is computed based on the features learned by the discriminator to help cDAL generate more accurate segmentation of discriminative regions in an input image. Additionally, we incorporated a random latent embedding into each layer of our model to significantly reduce the number of training and sampling time-steps, thereby making it much faster than other diffusion models for image segmentation
## Set up datasets ##
We trained cDAL on several datasets, including MoNuSeg2018, Chest-XRay(CXR) and Hippocampus.
## Training cDAL ##
We use the following commands on each dataset for training cDAL. Use ```parameters_monu.json``` for MonuSeg and ```parameters_lung.json``` for CXR.
To train the model for Hippocampus dataset, use this ```train_cDAL_hippo.py```. You can find corresponding parameters in the code.
To train either MoNuSeg or CXR, you should use ```train_cDal_monu_and_lung.py```. All necessary parameters are included in ```parameters_monu.json``` and ```parameters_lung.json```. These files can be directly loaded into the code, or you can modify parameters in the code file.
#### MoNuSeg ####
Here you can find general [website](https://monuseg.grand-challenge.org/) of the challenge,
download the dataset
[train](https://drive.google.com/file/d/1ZgqFJomqQGNnsx7w7QBzQQMVA16lbVCA/view?usp=sharing)
and [test](https://drive.google.com/file/d/1NKkSQ5T0ZNQ8aUhh0a8Dt2YKYCQXIViw/view?usp=sharing) sets.
MonuSeg dataset should have the following format, Therefore you need to preprocess the dataset before running the script.
Run this [Matlab Code](https://drive.google.com/file/d/1YDtIiLZX0lQzZp_JbqneHXHvRo45ZWGX/view) to convert the masks to PNG format:
```
MonuSeg/
Test/
img/
XX.tif
mask/
XX.png
Training/
img/
XX.tif
mask/
XX.png
```
#### CXR ####
This [link](https://www.kaggle.com/code/nikhilpandey360/lung-segmentation-from-chest-x-ray-dataset) is for Lung segmentation from Chest X-Ray dataset.
To preprocess images, we followed the same standard.
#### Hippocampus 3D ####
In this [link](http://medicaldecathlon.com/), you can find Hippocampus dataset. This dataset can be directly downloaded from this google drive [link](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2)
## Pretrained Checkpoints ##
All models are now available at the following link: ['saved_models'](https://huggingface.co/Hejrati/cDAL/tree/main).
Simply download the `saved_models` directory to the code directory. Use `parameters_monu.json` for MonuSeg and `parameters_lung.json` for CXR.
## Evaluation ##
After training, samples can be generated by calling ```sampling_monu_and_lung.py``` for MoNuSeg and CXR datasets or ```sampling_hippo.py``` for Hippocampus dataset.
Hippocamus uses ```metrics_hippo.py``` file for evaluation since it should be processed based on One-hot encoding.
We evaluated the models with a single NVIDIA Quadro RTX 6000 GPU.
We use the [MONAI](https://github.com/Project-MONAI/MONAI) implementation for Hippocampus dataset to process dataset and compute one-hot encoding. Aslo, we use [DDGAN](https://github.com/NVlabs/denoising-diffusion-gan/blob/main/train_ddgan.py) implemention for our difusion model and time-dependent discriminator.
## License ##
Please check the [LICENSE](https://github.com/Hejrati/cDAL/blob/master/LICENSE.txt) file.
## Bibtex ##
Cite our paper using the following bibtex item:
```
@inproceedings{hejrati2024conditional,
title={Conditional diffusion model with spatial attention and latent embedding for medical image segmentation},
author={Hejrati, Behzad and Banerjee, Soumyanil and Glide-Hurst, Carri and Dong, Ming},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={202--212},
year={2024},
organization={Springer}
}
```