@article{Majkowska2020,
  year = {2020},
  month = feb,
  publisher = {Radiological Society of North America ({RSNA})},
  volume = {294},
  number = {2},
  pages = {421--431},
  author = {Anna Majkowska and Sid Mittal and David F. Steiner and Joshua J. Reicher and Scott Mayer McKinney and Gavin E. Duggan and Krish Eswaran and Po-Hsuan Cameron Chen and Yun Liu and Sreenivasa Raju Kalidindi and Alexander Ding and Greg S. Corrado and Daniel Tse and Shravya Shetty},
  title = {Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation},
  journal = {Radiology}
}
@InProceedings{C2,
  author = 	 "Jones, C.D. and Smith, A.B. and Roberts, E.F.",
  title =        "Paper Title",
  booktitle =    "Proceedings Title",
  organization = "IEEE",
  year = 	 "2003",
  volume = 	 "II",
  pages = 	 "803-806"
}

@misc{irvin2019chexpert,
    title={CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison},
    author={Jeremy Irvin and Pranav Rajpurkar and Michael Ko and Yifan Yu and Silviana Ciurea-Ilcus and Chris Chute and Henrik Marklund and Behzad Haghgoo and Robyn Ball and Katie Shpanskaya and Jayne Seekins and David A. Mong and Safwan S. Halabi and Jesse K. Sandberg and Ricky Jones and David B. Larson and Curtis P. Langlotz and Bhavik N. Patel and Matthew P. Lungren and Andrew Y. Ng},
    year={2019},
    eprint={1901.07031},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@article{dbscan_eps,
	year = 2016,
	month = {Jan},
	publisher = {{IOP} Publishing},
	volume = {31},
	pages = {012012},
	author = {Nadia Rahmah and Imas Sukaesih Sitanggang},
	title = {Determination of Optimal Epsilon (Eps) Value on {DBSCAN} Algorithm to Clustering Data on Peatland Hotspots in Sumatra},
	journal = {{IOP} Conference Series: Earth and Environmental Science},
	abstract = {In this work we determine the optimal epsilon value on peatland on DBSCAN Algorithm to clustering data on peatland hotspots in sumatera. DBSCAN is a base algorithm for density based data clustering which contain noise and outliers. We found using this method that the area which has the highest density of hotspots in Sumatra in 2013 peatland is contained in cluster 1 of Riau Province that is equal to 2112 hotspots.}
}
@InProceedings{SugAno,
author="Yang, Lin
and Zhang, Yizhe
and Chen, Jianxu
and Zhang, Siyuan
and Chen, Danny Z.",
editor="Descoteaux, Maxime
and Maier-Hein, Lena
and Franz, Alfred
and Jannin, Pierre
and Collins, D. Louis
and Duchesne, Simon",
title="Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation",
booktitle="proceedings of MICCAI",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="399--407",
}


@article{dbscan,
  author    = {Daren Wang and
               Xinyang Lu and
               Alessandro Rinaldo},
  title     = {{DBSCAN:} Optimal Rates For Density-Based Cluster Estimation},
  journal   = {J. Mach. Learn. Res.},
  volume    = {20},
  pages     = {170:1--170:50},
  year      = {2019},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{ceal,
  author    = {Keze Wang and
               Dongyu Zhang and
               Ya Li and
               Ruimao Zhang and
               Liang Lin},
  title     = {Cost-Effective Active Learning for Deep Image Classification},
  journal   = {{IEEE} Trans. Circuits Syst. Video Technol.},
  volume    = {27},
  number    = {12},
  pages     = {2591--2600},
  year      = {2017},
  timestamp = {Tue, 25 Aug 2020 16:55:36 +0200},
  biburl    = {https://dblp.org/rec/journals/tcsv/WangZLZL17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{data_distillation,
  author    = {Ilija Radosavovic and
               Piotr Doll{\'{a}}r and
               Ross B. Girshick and
               Georgia Gkioxari and
               Kaiming He},
  title     = {Data Distillation: Towards Omni-Supervised Learning},
  booktitle = {2018 {IEEE} Conference on Computer Vision and Pattern Recognition,
               {CVPR} 2018, Salt Lake City, UT, USA, June 18-22, 2018},
  pages     = {4119--4128},
  publisher = {{IEEE} Computer Society},
  year      = {2018},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{resnext,
  author    = {Saining Xie and
               Ross B. Girshick and
               Piotr Doll{\'{a}}r and
               Zhuowen Tu and
               Kaiming He},
  title     = {Aggregated Residual Transformations for Deep Neural Networks},
  booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition,
               {CVPR} 2017, Honolulu, HI, USA, July 21-26, 2017},
  pages     = {5987--5995},
  publisher = {{IEEE} Computer Society},
  year      = {2017},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{sgd-nesterov,
  author    = {Ilya Sutskever and
               James Martens and
               George E. Dahl and
               Geoffrey E. Hinton},
  title     = {On the importance of initialization and momentum in deep learning},
  booktitle = {Proceedings of the 30th International Conference on Machine Learning,
               {ICML} 2013, Atlanta, GA, USA, 16-21 June 2013},
  series    = {{JMLR} Workshop and Conference Proceedings},
  volume    = {28},
  pages     = {1139--1147},
  year      = {2013},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{cyclic-lr,
  author    = {Leslie N. Smith},
  title     = {Cyclical Learning Rates for Training Neural Networks},
  booktitle = {2017 {IEEE} Winter Conference on Applications of Computer Vision,
               {WACV} 2017, Santa Rosa, CA, USA, March 24-31, 2017},
  pages     = {464--472},
  publisher = {{IEEE} Computer Society},
  year      = {2017},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{super-convergence,
  author    = {Leslie N. Smith and
               Nicholay Topin},
  title     = {Super-Convergence: Very Fast Training of Residual Networks Using Large
               Learning Rates},
  journal   = {CoRR},
  volume    = {abs/1708.07120},
  year      = {2017},
  archivePrefix = {arXiv},
  eprint    = {1708.07120},
  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{cb-loss,
  author    = {Yin Cui and
               Menglin Jia and
               Tsung{-}Yi Lin and
               Yang Song and
               Serge J. Belongie},
  title     = {Class-Balanced Loss Based on Effective Number of Samples},
  booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
               2019, Long Beach, CA, USA, June 16-20, 2019},
  pages     = {9268--9277},
  publisher = {Computer Vision Foundation / {IEEE}},
  year      = {2019},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{deep-face,
  author    = {Yaniv Taigman and
               Ming Yang and
               Marc'Aurelio Ranzato and
               Lior Wolf},
  title     = {DeepFace: Closing the Gap to Human-Level Performance in Face Verification},
  booktitle = {2014 {IEEE} Conference on Computer Vision and Pattern Recognition,
               {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014},
  pages     = {1701--1708},
  publisher = {{IEEE} Computer Society},
  year      = {2014},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{face-net,
  author    = {Florian Schroff and
               Dmitry Kalenichenko and
               James Philbin},
  title     = {FaceNet: {A} unified embedding for face recognition and clustering},
  booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
               2015, Boston, MA, USA, June 7-12, 2015},
  pages     = {815--823},
  publisher = {{IEEE} Computer Society},
  year      = {2015},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@manual{pneumonia-dataset,
  title  = "RSNA Pneumonia Detection Challenge",
  author = "https://www.kaggle.com/c/rsna-pneumonia-detection-challenge",
}