Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications

Haohan Wang, Zhenglin Wu, Eric P. Xing. Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications. In Russ B. Altman, A. Keith Dunker, Lawrence Hunter, Marylyn D. Ritchie, Teri E. Klein, editors, Biocomputing 2019: Proceedings of the Pacific Symposium, The Big Island of Hawaii, Hawaii, USA, January 3-7, 2019. pages 54-65, 2019. [doi]

@inproceedings{WangWX19,
  title = {Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications},
  author = {Haohan Wang and Zhenglin Wu and Eric P. Xing},
  year = {2019},
  url = {http://psb.stanford.edu/psb-online/proceedings/psb19/wang.pdf},
  researchr = {https://researchr.org/publication/WangWX19},
  cites = {0},
  citedby = {0},
  pages = {54-65},
  booktitle = {Biocomputing 2019: Proceedings of the Pacific Symposium, The Big Island of Hawaii, Hawaii, USA, January 3-7, 2019},
  editor = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Marylyn D. Ritchie and Teri E. Klein},
}