Welcome to the official implementation of ``SvANet: A Scale-variant Attention-based Network for Small Medical Object Segmentation''. This repository offers a robust toolkit designed for advanced tasks in deep learning and computer vision, specifically tailored for semantic segmentation. It supports features such as training progress visualization, logging, and calculation of standard metrics.
SvANet: A Scale-variant Attention-based Network for Small Medical Object Segmentation
Wei Dai, Rui Liu, Zixuan Wu, Tianyi Wu, Min Wang, Junxian Zhou, Yixuan Yuan, Jun Liu
Under review by the IEEE Transactions on Medical Imaging, 2024. [arXiv]
To install the SvANet implementation, please follow the detailed instructions in INSTALL.md.
Please refer to DATA.md for guidelines on preparing the datasets for benchmarking and training.
Execute the training and evaluation processes using the configuration settings in main.sh script.
For detailed settings of the ablation study and additional experiments, refer to refer to the scripts ablation.sh and ablation_extra.sh.
If you use this implementation in your research, please consider citing our paper as follows:
@misc{dai2024svanet,
title={SvANet: A Scale-variant Attention-based Network for Small Medical Object Segmentation},
author={Dai, Wei and Liu, Rui and Wu, Zixuan and Wu, Tianyi and Wang, Min and Zhou, Junxian and Yuan, Yixuan and Liu, Jun},
year={2024},
eprint={2407.07720},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2407.07720},
}