Deep learning of feature representation with multiple instance learning for medical image analysis

@article{Xu2014DeepLO,
  title={Deep learning of feature representation with multiple instance learning for medical image analysis},
  author={Yan Xu and Tao Mo and Qiwei Feng and Peilin Zhong and Maode Lai and Eric I-Chao Chang},
  journal={2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2014},
  pages={1626-1630}
}
  • Yan XuTao Mo E. Chang
  • Published 4 May 2014
  • Computer Science
  • 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
This paper studies the effectiveness of accomplishing high-level tasks with a minimum of manual annotation and good feature representations for medical images. [] Key Method We use multiple instance learning (MIL) framework in classification training with deep learning features. Several interesting conclusions can be drawn from our work: (1) automatic feature learning outperforms manual feature; (2) the unsupervised approach can achieve performance that's close to fully supervised approach (93.56%) vs. (94…

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