Classification networks pre-trained on ImageNet and RadImageNet for expainable brain tumor detection on the Cheng et al. dataset
Cheng et al. dataset can be downloaded from: https://figshare.com/articles/brain_tumor_dataset/1512427
RadImageNet PyTorch ResNet50 weights can be downloaded from: https://github.com/BMEII-AI/RadImageNet
Just run train_XAIMed_Net.py training script where you can select the corresponding model
To use CRF install pydensecrf
Then run compute_metrics.py
Proposed Architecture of the XAIMed-Net:
Qualitative results so far:
Second row: SEA1 ImageNet
Third row: SEA3 RadImageNet
Last row: B2-Net trained from scratch
EgenCAM code is adapted from https://github.com/jacobgil/pytorch-grad-cam
Acknowledging this work
If you would like to cite our work, please use the following reference:
Oluwabukola Adegboro, Vayangi Ganepola, Julia Dietlmeier, Claudia Mazo, Noel E. O'Connor. "XAIMed-Net: Towards Explainable Brain Tumour Detection in 2D T1-weighted CE-MRI Images Using Transfer Learning". 26th Irish Machine Vision and Image Processing Conference, University of Limerick, Ireland. August 21st, 2024 - August 23rd, 2024.