In the world of medical imaging, the capability to generate accurate immunohistochemical images is crucial. The BCI project leverages the Pyramid Pix2Pix model to create these images, facilitating better research and analysis in breast cancer diagnostics. This guide will take you step-by-step through the process of setting up and using the BCI model for image generation.

Getting Started with Pyramid Pix2Pix

• Ensure you have a suitable environment: Linux, Python version 3.6, and either a CPU or NVIDIA GPU with CUDA CuDNN.
• Clone the BCI repository and install the required Python packages.

git clone https://github.com/bupt-ai-cz/BCI
cd PyramidPix2pix
pip install -r requirements.txt

Preparing Your Dataset

To successfully train the model, you need the BCI dataset. You will create pairs of HE and IHC images, where each pair represents the same scene in two different styles. Here’s how to do it:

• Download the BCI dataset from the homepage.
• Organize your images into a specific directory structure:

pathtodata/
└── datasets/
    ├── A/
    │   ├── train/
    │   ├── val/
    │   └── test/
    └── B/
        ├── train/
        ├── val/
        └── test/

All images in pairs must have corresponding filenames and the same dimensions, for instance, pathtodata/A/train/image1.jpg should correspond to pathtodata/B/train/image1.jpg.

python datasets/combine_A_and_B.py --fold_A pathtodata/A --fold_B pathtodata/B --fold_AB pathtodata

Training the Model

Once your dataset is prepared, you can begin training the model. You have the option to train it at different resolutions based on your GPU capacity.

• For full resolution (1024×1024):

python train.py --dataroot ./datasets/BCI --gpu_ids 0 --pattern L1_L2_L3_L4

• For reduced GPU memory usage (512×512):

python train.py --dataroot ./datasets/BCI --preprocess crop --crop_size 512 --gpu_ids 0 --pattern L1_L2_L3_L4

Testing the Model

After the model is trained, you can test it to generate images. It is recommended to test at full resolution for more accurate results:

python test.py --dataroot ./datasets/BCI --gpu_ids 0

Evaluating Your Results

To assess the performance of your model, calculate the average PSNR and SSIM with the following command:

python evaluate.py --result_path ./results/pyramidpix2pix

Troubleshooting

If you encounter issues during the setup or execution of the above commands, consider the following:

• Make sure your Python environment is activated and all required packages are installed.
• Double-check that your image pairs are correctly matched and of the same dimensions.
• If you face memory errors during training, consider reducing the batch size or opting for lower image resolutions.
• For more insights, updates, or to collaborate on AI development projects, stay connected with [fxis.ai](https://fxis.ai).

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.