conda env create -f cvt.yaml
conda activate cvt
conda env create -f unetr.yaml
conda activate unetr
For datasets from ADNI(Alzheimer's Disease Neuroimaging Initiative) that include PET(Positron emission tomography) scans.
Data Collection/full_FDG/
Your dataset directory should be structured as follows:
- training set: /root_dir/ADNI/Dynamic/Resolution/
- validation set: /root_dir/ADNI/Averaged/Resolution/
| Scanner | Resolution | Voxel spacing | Manufacturer | Institution |
|---|---|---|---|---|
| 1 | (192, 192, 136) | (1.21875, 1.21875, 1.21875) | Siemens | Univ of California |
| 2 | (192, 192, 128) | (1.21875, 1.21875, 1.21875) | Siemens | Univ of California |
| 3 | (224, 224, 81) | (1.01821, 1.01821, 2.02699) | Siemens | Univ of California |
| 4 | (128, 128, 90) | (2, 2, 2) | Philips Healthcare | OHSU |
| 5 | (128, 128, 63) | (2.05941, 2.05941, 2.425) | Siemens | UCSD |
To create an json file for efficient data split, run the following command:
python3 data/make_json.py
Click the links below to download the pre-trained weights for each of the five scanners. Each scanner has weights for three folds. Training details are described in our paper. Currently, available versions of pre-trained weights are as follows:
sh shell/train/pretraining.sh
sh shell/train/tuning.sh
Using Low-Rank Adaptation (LoRA) with Conv3D involves modifying the Conv3D layers to integrate the LoRA technique.
- Modified to place Lora instead of Convolutional Projection in Attention
- nn.Conv3d -> lora.Conv3d
import loralib as lora
ADNI data cannot be publicly shared by private users. It was not possible to directly upload data in this repository. However, feel free to contact me if you need more information about how to access data or about data used.
Remember to follow the ADNI data sharing and publication policy, if you are going to publish a work in which data from the ADNI database were used.