Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data

@article{Lai2021HeteroModalLA,
  title={Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data},
  author={Bolin Lai and Yuhsuan Wu and Xiao-Yun Zhou and Peng Wang and Le Lu and Lingyun Huang and Mei Han and Jing Xiao and Heping Hu and Adam P. Harrison},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.12972}
}
Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advances and increased data availability. However, the high costs of annotations hinder the collection of large and completely labeled datasets, motivating semi-supervised detection approaches. In this paper, we introduce mean teacher hetero-modal detection (MTHD), which addresses two important gaps in current semi-supervised detection. First, it is not obvious how to… 

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