Awesome
<p align="center"> <img src="images/T-Mamba-logo.png" width="150"> </p>T-Mamba
Jing Hao, Lei He, Kuo Feng Hung.
This repository is the official implementation of the T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentation .
More experiments are running 🔥
We are conducting more experiments and analysis on 3D CBCT and 2D X-ray images, and will update the whole manuscript. 🏃♂️
The code, pre-trained weights, and datasets will be fully available.
Currently, our T-Mamba supports 2D & 3D vision tasks. Welcome to try it for improving your model's performance.
The proposed TED3 dataset is available at: Hugging Face.
If u have any quesitons, pls feel free to drop me via isjinghao@gmail.com.
Install
conda create -n tmamba python=3.9
conda activate tmamba
pip install -r requirements.txt
cd ../causal-conv1d
python setup.py install
cd Vim-main/mamba
python setup.py install
=============================
Requirement specific version:
mamba_ssm==1.0.1
causal_conv1d==1.0.0
=============================
Training
sh train_3d.sh # for 3D
sh train_2d.sh # for 2D
Testing (for evaluations)
sh test_3d.sh # for 3D
sh test_2d.sh # for 2D
Inference
sh infer_3d.sh # for 3D
sh infer_2d.sh # for 2D