A General-Purpose Multimodal Foundation Model for Dermatology [Arxiv Paper] [Cite]

We introduce PanDerm, a powerful multimodal dermatology foundation model that can seamlessly interpret multiple imaging modalities and master diverse dermatological tasks from cancer screening, diagnosis to long-term prognosis.

First, clone the repo and cd into the directory:

git clone https://github.com/SiyuanYan1/PanDerm
cd PanDerm

Then create a conda env and install the dependencies:

conda create -n PanDerm python=3.10 -y
conda activate PanDerm
pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt

Download the pre-trained model weights from this Google Drive link.

After downloading, you need to update the model weights path in the code:

1. Open the file PanDerm/linear_probe/models/builder.py
2. Locate line 42
3. Replace the existing path with the directory where you saved the model weights:

root_path = '/path/to/your/PanDerm/Model_Weights/'

We've pre-processed the public datasets used in this study. To reproduce the results reported in our paper and prevent data leakage between splits, please use these processed datasets.

If you wish to use our model with your own dataset, please organize it in the same format as these pre-processed datasets.

Note: The processed datasets may differ slightly from those provided on the official websites. To ensure reproducibility of our paper's results, please use the processed data links provided above.

Training and evaluation using HAM10000 as an example. Replace csv path and root path with your own dataset.

• nb_classes: Set this to the number of classes in your evaluation dataset.
• batch_size: Adjust based on the memory size of your GPU.
• percent_data: Controls the percentage of training data used. For example, 0.1 means evaluate models using 10% training data. Modify this if you want to conduct label efficiency generalization experiments.

cd linear_probe
CUDA_VISIBLE_DEVICES=0 python linear_eval.py \
  --batch_size 1000 \
  --model 'PanDerm' \
  --nb_classes 7 \
  --percent_data 1.0 \
  --csv_filename 'PanDerm_results.csv' \
  --output_dir "/path/to/your/PanDerm/LP_Eval/output_dir2/ID_Res/PanDerm_res/" \
  --csv_path "/path/to/your/PanDerm/Evaluation_datasets/HAM10000_clean/ISIC2018_splits/HAM_clean.csv" \
  --root_path "/path/to/your/PanDerm/Evaluation_datasets/HAM10000_clean/ISIC2018/"

For additional evaluation datasets, please refer to the bash scripts for detailed usage. We provide running code to evaluate on 9 public datasets. You can choose the model from the available options.

To run the evaluations:

cd linear_probe
bash script/lp.sh

Check out our easy-to-follow Jupyter Notebook:

HAM_clean_evaluation.ipynb

This notebook shows you how to:

• Load our pre-trained model
• Use it for feature extraction
• Perform basic classification

Please refer to details here.

@misc{yan2024generalpurposemultimodalfoundationmodel,
      title={A General-Purpose Multimodal Foundation Model for Dermatology}, 
      author={Siyuan Yan and Zhen Yu and Clare Primiero and Cristina Vico-Alonso and Zhonghua Wang and Litao Yang and Philipp Tschandl and Ming Hu and Gin Tan and Vincent Tang and Aik Beng Ng and David Powell and Paul Bonnington and Simon See and Monika Janda and Victoria Mar and Harald Kittler and H. Peter Soyer and Zongyuan Ge},
      year={2024},
      eprint={2410.15038},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2410.15038}, 
}