tumor-detection
Tumor-Detection
Please install PyTorch Cuda version & requirements.txt to run the code.
A 3D segmentation task:
python3 train3d.py --task brats --split all --bs 2 --maxiter 10000 --randscale 0.1 --net segtran --attractors 1024 --translayers 1
python3 test3d.py --task brats --split all --bs 5 --ds 2019valid --net segtran --attractors 1024 --translayers 1 --cpdir ../model/segtran-brats-2019train-01170142 --iters 8000
Arguments:
--net: which type of model to use. Currently three 3D segmentation models can be chosen from. unet: 3D U-Net. vnet: V-Net. segtran: Squeeze-and-Expansion transformer for segmentation.
--bb: the type of CNN backbone for segtran. A commonly used 3D backbone is i3d (default).
--attractors: the number of attractors in the Squeezed Attention Block.
To save GPU RAM, 3D tasks usually only use one transformer layer, i.e., --translayers 1.
Accuracy achieved for BRATS 2019
Acknowledgement
The "receptivefield" folder is from https://github.com/fornaxai/receptivefield/, with minor edits and bug fixes.
The "efficientnet" folder is from https://github.com/lukemelas/EfficientNet-PyTorch, with minor customizations.
The "networks/setr" folder is a slimmed-down version of https://github.com/fudan-zvg/SETR/, with a few custom config files.
There are a few baseline models under networks/ which were originally implemented in various github repos. Here I won’t acknowlege them individually.
Some code under "dataloaders/" (esp. 3D image preprocessing) was borrowed from https://github.com/yulequan/UA-MT.