Multi-Task Semi-Supervised Learning For Pulmonary Lobe Segmentation

@article{Jia2021MultiTaskSL,
  title={Multi-Task Semi-Supervised Learning For Pulmonary Lobe Segmentation},
  author={Jingnan Jia and Zhiwei Zhai and M. Els Bakker and Irene Hernandez-Giron and Marius Staring and Berend C. Stoel},
  journal={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  year={2021},
  pages={1329-1332}
}
  • Jingnan JiaZ. Zhai B. Stoel
  • Published 13 April 2021
  • Computer Science
  • 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Pulmonary lobe segmentation is an important preprocessing task for the analysis of lung diseases. Traditional methods relying on fissure detection or other anatomical features, such as the distribution of pulmonary vessels and airways, could provide reasonably accurate lobe segmentations. Deep learning based methods can outperform these traditional approaches, but require large datasets. Deep multi-task learning is expected to utilize labels of multiple different structures. However, commonly… 
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