ML Anthology

FocalMix: Semi-Supervised Learning for 3D Medical Image Detection

Dong Wang, Yuan Zhang, Kexin Zhang, Liwei Wang
CVPR 2020
doi:10.1109/CVPR42600.2020.00401 /cvpr/2020/wang2020cvpr-focalmix/

Abstract

Applying artificial intelligence techniques in medical imaging is one of the most promising areas in medicine. However, most of the recent success in this area highly relies on large amounts of carefully annotated data, whereas annotating medical images is a costly process. In this paper, we propose a novel method, called FocalMix, which, to the best of our knowledge, is the first to leverage recent advances in semi-supervised learning (SSL) for 3D medical image detection. We conducted extensive experiments on two widely used datasets for lung nodule detection, LUNA16 and NLST. Results show that our proposed SSL methods can achieve a substantial improvement of up to 17.3% over state-of-the-art supervised learning approaches with 400 unlabeled CT scans.

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Cite

Text

Wang et al. "FocalMix: Semi-Supervised Learning for 3D Medical Image Detection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00401

Markdown

[Wang et al. "FocalMix: Semi-Supervised Learning for 3D Medical Image Detection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/wang2020cvpr-focalmix/) doi:10.1109/CVPR42600.2020.00401

BibTeX

@inproceedings{wang2020cvpr-focalmix,
  title     = {{FocalMix: Semi-Supervised Learning for 3D Medical Image Detection}},
  author    = {Wang, Dong and Zhang, Yuan and Zhang, Kexin and Wang, Liwei},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2020},
  doi       = {10.1109/CVPR42600.2020.00401},
  url       = {https://mlanthology.org/cvpr/2020/wang2020cvpr-focalmix/}
}