Models

A paper draft is available at arXiv:2501.11535. The objective of this paper is to provide a baseline for performing multi-modal data classification on a novel open multimodal dataset of hepatocellular carcinoma (HCC), which includes both image data (contrast-enhanced CT and MRI images) and tabular data (the clinical laboratory test data as well as case report forms).

Example models and pre-processed data are provided at github.com/ZhaoWenzhao/QHCC.

In order to run the provided example scripts, you will need to use Python 3.8 or 3.9, and install the required Python libraries.

pip & requirements.txt

You can do this with pip, by first downloading requirements.txt and then running:

pip

pip install -r requirements.txt

conda & environment.yml

You could also use conda or micromamba, by first downloading environment.yml and then running:

condamicromamba

conda env create -f environment.yml
conda activate QHCC

micromamba env create -f environment.yml
micromamba activate QHCC

direct pip/conda

Alternatively you can install the required Python libraries directly:

pipcondamicromamba

pip install jupyter numpy pandas pooch pyradiomics simpleitk "scikit-learn<1.6" xgboost

conda env create -n QHCC jupyter numpy pandas pooch python radiomics::pyradiomics simpleitk "scikit-learn<1.6" xgboost -c conda-forge -c radiomics
conda activate QHCC

micromamba env create -n QHCC jupyter numpy pandas pooch python radiomics::pyradiomics simpleitk "scikit-learn<1.6" xgboost -c conda-forge -c radiomics
micromamba activate QHCC