@ARTICLE{gev,
  author={Joshua Bridge and Yanda Meng and Yitian Zhao and Yong Du and Mingfeng Zhao and Renrong Sun and Yalin Zheng},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={{Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models}}, 
  year={2020},
  volume={24},
  number={10},
  pages={2776-2786},
  doi={10.1109/JBHI.2020.3012383}
}

@InProceedings{softmax,
author="Bridle, John S.",
editor="Souli{\'e}, Fran{\c{c}}oise Fogelman
and H{\'e}rault, Jeanny",
title="Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition",
booktitle="Neurocomputing",
year="1990",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="227--236",
abstract="We are concerned with feed-forward non-linear networks (multi-layer perceptrons, or MLPs) with multiple outputs. We wish to treat the outputs of the network as probabilities of alternatives (e.g. pattern classes), conditioned on the inputs. We look for appropriate output non-linearities and for appropriate criteria for adaptation of the parameters of the network (e.g. weights). We explain two modifications: probability scoring, which is an alternative to squared error minimisation, and a normalised exponential (softmax) multi-input generalisation of the logistic non-linearity. The two modifications together result in quite simple arithmetic, and hardware implementation is not difficult either. The use of radial units (squared distance instead of dot product) immediately before the softmax output stage produces a network which computes posterior distributions over class labels based on an assumption of Gaussian within-class distributions. However the training, which uses cross-class information, can result in better performance at class discrimination than the usual within-class training method, unless the within-class distribution assumptions are actually correct.",
isbn="978-3-642-76153-9"
}

@article{covid,
  title={Covid-19 image data collection: Prospective predictions are the future},
  author={Cohen, Joseph Paul and Morrison, Paul and Dao, Lan and Roth, Karsten and Duong, Tim Q and Ghassemi, Marzyeh},
  journal={arXiv preprint arXiv:2006.11988},
  year={2020}
}

@article{xray,
  title={Identifying medical diagnoses and treatable diseases by image-based deep learning},
  author={Kermany, Daniel S and Goldbaum, Michael and Cai, Wenjia and Valentim, Carolina CS and Liang, Huiying and Baxter, Sally L and McKeown, Alex and Yang, Ge and Wu, Xiaokang and Yan, Fangbing and others},
  journal={Cell},
  volume={172},
  number={5},
  pages={1122--1131},
  year={2018},
  publisher={Elsevier}
}


@inproceedings{lin,
  title={{Focal Loss for Dense Object Detection}},
  author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
  booktitle={{Proceedings of the IEEE International Conference on Computer Vision}},
  pages={2980--2988},
  year={2017}
}

@inproceedings{iclr,
title={Long-tail Learning via Logit Adjustment},
author={Aditya Krishna Menon and Sadeep Jayasumana and Ankit Singh Rawat and Himanshu Jain and Andreas Veit and Sanjiv Kumar},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=37nvvqkCo5}
}


@inproceedings{cui,
  author={Y. Cui and M. Jia and T. Lin and Y. Song and S. Belongie},
  booktitle={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Class-Balanced Loss Based on Effective Number of Samples}, 
  year={2019},
  pages={9260-9269},
  doi={10.1109/CVPR.2019.00949}}


@inproceedings{iv3,
  title={Rethinking the inception architecture for computer vision},
  author={Szegedy, Christian and Vanhoucke, Vincent and Ioffe, Sergey and Shlens, Jon and Wojna, Zbigniew},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2818--2826},
  year={2016}
}






