ba-transformer's Introduction

Boundary-aware Transformers for Skin Lesion Segmentation

Recently, transformers have been proposed as a promising tool for global context modeling by employing a powerful global attention mechanism, but one of their main shortcomings when applied to segmentation tasks is that they cannot effectively extract sufficient local details to tackle ambiguous boundaries. We propose a novel boundary-aware transformer (BAT) to comprehensively address the challenges of automatic skin lesion segmentation.

This paper has been accepted by MICCAI. Get the full paper on Arxiv.

Fig. 1. Structure of BAT.

Code List

Network
Pre-processing
Training Codes
MS

For more details or any questions, please feel easy to contact us by email ^_^

Usage

First, you can download the dataset from ISIC challenge.
Second, for pre-processing the dataset, you can run:

$ python src/resize.py
$ python src/point_gen.py

Third, before running the network, you should first download the code of CELL_DETR into lib and install it.
In the end, for testing the model, you could run:

$ python net/trans_deeplab.py

TODO

We will update the latest training version under the same setting as CA-Net.

Citation

If you find BAT useful in your research, please consider citing:

@inproceedings{wang2021boundary,
  title={Boundary-Aware Transformers for Skin Lesion Segmentation},
  author={Wang, Jiacheng and Wei, Lan and Wang, Liansheng and Zhou, Qichao and Zhu, Lei and Qin, Jing},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={206--216},
  year={2021},
  organization={Springer}
}

Recommend Projects