By Neha Gianchandani (& co-authors)
This is the official repository for the papers titled above.
Status: Contribution 1 - Presented at MLMI workshop @ MICCAI 2023. Published in Springer. (2023)
Contribution 1-4 - Published in MELBA (2024)
It is highly recommended to go through all code and replace any placeholders (XXXX) with appropriate paths.
python main.py --learning_rate 0.001 --batch_size 3 --epochs 300 --root_dir #add root directory#
python main_test.py --root_dir #add root directory# --checkpoint_path #add path to saved model .pth file#
@inproceedings{gianchandani2023multitask,
title={A multitask deep learning model for voxel-level brain age estimation},
author={Gianchandani, Neha and Ospel, Johanna and MacDonald, Ethan and Souza, Roberto},
booktitle={International Workshop on Machine Learning in Medical Imaging},
pages={283--292},
year={2023},
organization={Springer}
}
@article{melba:2024:007:gianchandani,
title = "A voxel-level approach to brain age prediction: A method to assess regional brain aging",
author = "Gianchandani, Neha and Dibaji, Mahsa and Ospel, Johanna and Vega, Fernando and Bento, Mariana and MacDonald, M. Ethan and Souza, Roberto",
journal = "Machine Learning for Biomedical Imaging",
volume = "2",
issue = "April 2024 issue",
year = "2024",
pages = "761--795",
issn = "2766-905X",
doi = "https://doi.org/10.59275/j.melba.2024-4dg2",
url = "https://melba-journal.org/2024:007"
}