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This is a project by team MediMiners. Team Members: Ganesh, Hasibur, Mizanur, and Sazed (Order is insignificant)

Dataset Link

https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification/

Environment for Densenet and Data Preprocessing

Please install the required packages using pip install -r requirement.txt in the corresponding conda environment.

DICOM to NIFTII

To convert the DICOM format to NIFTII format, please use dicomtoniftii.ipynb under the preprocessing folder.

Densent 169

For central preprocessing and interval preprocessing, please utilize the corresponding codes. Please select the exact MRI type and provide the accurate data path for smooth execution. The results will be saved in a CSV file for further evaluation and ensembling.

Ensemble and ROC Curve

The ROC curve is generated differently in the dm_auc_generation.ipynb file. The ROC curve generator takes the predicted values for different models or MRI types, along with their maximum, minimum, and average values, which are provided in the corresponding CSV files in the Prediction_CSV folder.

Radiomic Feature

the conda environment for the radiomic feature code is radiomic.yml

you can download default vit_h from https://github.com/facebookresearch/segment-anything?tab=readme-ov-file#model-checkpoints

For Radiomic Feature Code run these files sequentially

  1. 3dMaskGen.py
  2. radDataCreation.py
  3. radiomicClassifierUpdated.py

Note: ensure that all the data paths in those file is accurate. The code is done by using absolute path