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---
license: mit
tags:
- keras
- medical-imaging
- deep-learning
- .h5-model
framework: keras
task: image-translation
---
# pyMEAL: Multi-Encoder-Augmentation-Aware-Learning
pyMEAL is a multi-encoder framework for augmentation-aware learning that accurately performs CT-to-T1-weighted MRI translation under diverse augmentations. It utilizes four dedicated encoders and three fusion strategies, concatenation (CC), fusion layer (FL), and controller block (BD), to capture augmentation-specific features. MEAL-BD outperforms conventional augmentation methods, achieving SSIM > 0.83 and PSNR > 25 dB in CT-to-T1w translation.
## Dependecies
tensorflow
matplotlib
SimpleITK
scipy
antspyx
---
## Available Models
| Model ID | File Name | Description |
|----------|------------------------------------------------|---------------------------------------------|
| BD | `builder1_mode1l1abW512_1_11211z1p1rt_.h5` | Builder-based architecture model |
| CC | `best_moderRl_RHID2_1mo.h5` | Encoder-concatenation-based configuration |
| FL | `bestac22_mode3l_512m2_m21.h5` | Feature-level fusion-based model |
| NA | `direct7_11ag23f11.h5` | Direct training baseline model |
| TA | `best_modelaf2ndab7_221ag12g11.h5` | traditional augmentation configuration model|
---
### Model Architecture Overview
*Figure 1. Model architecture for the model having no augmentation and traditional augmentation.*
*Figure 2. Model architecture for Multi-Stream with a Builder Controller block method (BD), Fusion layer (FL), and Encoder concatenation (CC).*
## Download Model Files
You can download any `.h5` file directly:
- [Download builder1_mode1l1abW512_1_11211z1p1rt_.h5](https://huggingface.co/AI-vBRAIN/pyMEAL/resolve/main/builder1_mode1l1abW512_1_11211z1p1rt_.h5)
- [Download best_moderRl_RHID2_1mo.h5](https://huggingface.co/AI-vBRAIN/pyMEAL/resolve/main/best_moderRl_RHID2_1mo.h5)
- [Download bestac22_mode3l_512m2_m21.h5](https://huggingface.co/AI-vBRAIN/pyMEAL/resolve/main/bestac22_mode3l_512m2_m21.h5)
- [Download direct7_11ag23f11.h5](https://huggingface.co/AI-vBRAIN/pyMEAL/resolve/main/direct7_11ag23f11.h5)
- [Download best_modelaf2ndab7_221ag12g11.h5](https://huggingface.co/AI-vBRAIN/pyMEAL/resolve/main/best_modelaf2ndab7_221ag12g11.h5)
---
## How to Use
### Load a Model (Basic)
```python
import tensorflow as tf
# Load the model
model = tf.keras.models.load_model("model.h5", compile=False)
# Run inference
output = model.predict(input_data)
```
Here, `input_data` refers to a CT image, and the corresponding T1-weighted (T1w) image is produced as the output.
For detailed instructions on how to use each module of the **pyMEAL** software, please refer to the [tutorial section on our GitHub repository](https://github.com/ai-vbrain/pyMEAL).
### How to cite these models?
Please cite the following:
```python
@article{ilyas2025pymeal,
title={pyMEAL: A Multi-Encoder Augmentation-Aware Learning for Robust and Generalizable Medical Image Translation},
author={Ilyas, Abdul-mojeed Olabisi and Maradesa, Adeleke and Banzi, Jamal and Huang, Jianpan and Mak, Henry KF and Chan, Kannie WY},
journal={arXiv preprint arXiv:2505.24421},
year={2025}
}
```
### How to Get Support?
For help, contact:
- Dr. Ilyas (<amoiIyas@hkcoche.org>)
- Dr. Maradesa (<amaradesa@hkcoche.org>)
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