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chestx-ray8-datasheet.bib
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% Encoding: UTF-8
@misc{PAIR,
author = {{PAIR with Google}},
date = {2020},
title = {{Measuring Fairness}},
year = {2020},
howpublished = {\href{https://pair.withgoogle.com/explorables/measuring-fairness}{Link to publication 2020-06-05}},
url = {https://pair.withgoogle.com/explorables/measuring-fairness},
urldate = {2020-06-05}
}
@article{Mulligan2019,
author = {Deirdre K. Mulligan and Joshua A. Kroll and Nitin Kohli and Richmond Y. Wong},
title = {{This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology}},
number = {CSCW},
pages = {1--36},
url = {https://arxiv.org/pdf/1909.11869.pdf},
volume = {3},
journal = {Proceedings of the ACM on Human-Computer Interaction},
publisher = {ACM New York, NY, USA},
year = {2019}
}
@article{Allen2019,
author = {James A. Allen},
title = {{The Color of Algorithms: An Analysis and Proposed Research Agenda for Deterring Algorithmic Redlining}},
pages = {219},
volume = {46},
journal = {Fordham Urban Law Journal},
publisher = {HeinOnline},
year = {2019}
}
@book{Barocas2019,
author = {Solon Barocas and Moritz Hardt and Arvind Narayanan},
date = {2019-12-06},
title = {{Fairness and Machine Learning}},
publisher = {fairmlbook.org},
url = {https://fairmlbook.org/},
urldate = {2020-06-05}
}
@article{Committee1990,
author = {{IEEE Standards Committee}},
date = {1990},
title = {{IEEE Standard Glossary of Software Engineering Terminology 610.12-1990}},
year = {1990},
url = {https://ieeexplore.ieee.org/document/159342},
urldate = {2020-08-07}
}
@misc{Ng2019,
author = {Andrew Ng},
date = {2019-11-06},
title = {{The Batch November 6, 2019}},
year = {2019},
howpublished = {\href{https://info.deeplearning.ai/the-batch-deepmind-masters-starcraft-2-ai-attacks-on-amazon-a-career-in-robot-management-banks-embrace-bots-1}{Link to publication 2020-06-13}},
url = {https://info.deeplearning.ai/the-batch-deepmind-masters-starcraft-2-ai-attacks-on-amazon-a-career-in-robot-management-banks-embrace-bots-1},
urldate = {2020-06-13}
}
@book{Osoba2019,
author = {Osoba, Osonde A. and Boudreaux, Benjamin and Saunders, Jessica and Irwin, J. Luke and Mueller, Pam A. and Cherney, Samantha},
title = {{Algorithmic Equity: A Framework for Social Applications}},
publisher = {RAND Corporation},
year = {2019}
}
@book{RoyalSociety2017,
author = {{The Royal Society}},
title = {{Machine learning : the power and promise of computers that learn by example}},
editor = {The Royal Society},
isbn = {9781782522591},
publisher = {The Royal Society},
url = {https://royalsociety.org/~/media/policy/projects/machine-learning/publications/machine-learning-report.pdf},
urldate = {2020-06-28},
year = {2017}
}
@misc{Gangadharan2014,
author = {Seeta Peña Gangadharan},
date = {2014-10-27},
title = {{Data and Discrimination Collected Essays}},
year = {2014},
howpublished = {\href{https://www.newamerica.org/oti/policy-papers/data-and-discrimination/}{Link to publication 2020-06-14}},
url = {https://www.newamerica.org/oti/policy-papers/data-and-discrimination/},
urldate = {2020-06-14}
}
@book{Munro2020,
author = {Munro, Robert},
date = {2020-12-29},
title = {{Human-In-The-Loop Machine Learning}},
isbn = {1617296740},
pagetotal = {325},
publisher = {MANNING PUBN},
url = {https://www.ebook.de/de/product/39016207/robert_munro_human_in_the_loop_machine_learning.html},
ean = {9781617296741},
year = {2020}
}
@misc{FAT/ML,
author = {FAT/ML},
title = {{Principles for Accountable Algorithms and a Social Impact Statement for Algorithms}},
year = {2020},
howpublished = {\href{https://www.fatml.org/resources/principles-for-accountable-algorithms}{Link to publication 2020-06-14}},
url = {https://www.fatml.org/resources/principles-for-accountable-algorithms},
urldate = {2020-06-16}
}
@article{Suresh2019,
author = {Suresh, Harini and Guttag, John V.},
title = {{A framework for understanding unintended consequences of machine learning}},
journal = {arXiv preprint arXiv:1901.10002},
year = {2019}
}
@misc{Medscape2020,
author = {Medscape},
date = {2020-05-20},
title = {{Medscape Radiologist Compensation Report 2020}},
year = {2020},
howpublished = {\href{https://www.medscape.com/slideshow/2020-compensation-radiologist-6012747}{Link to publication 2020-06-20}},
url = {https://www.medscape.com/slideshow/2020-compensation-radiologist-6012747},
urldate = {2020-06-20}
}
@misc{Economist2020a,
author = {{The Economist}},
date = {2020-06-13},
editor = {{The Economist}},
title = {{Artificial Intelligence and its Limits - Autumn is coming}},
year = {2020},
howpublished = {\href{https://www.economist.com/technology-quarterly/2020/06/11/humans-will-add-to-ais-limitations}{Link to publication 2020-06-20}},
url = {https://www.economist.com/technology-quarterly/2020/06/11/humans-will-add-to-ais-limitations},
urldate = {2020-06-20}
}
@article{Zech2018,
author = {John R. Zech and Marcus A. Badgeley and Manway Liu and Anthony B. Costa and Joseph J. Titano and Eric Karl Oermann},
title = {{Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study}},
year = {2018},
doi = {10.1371/journal.pmed.1002683},
editor = {Aziz Sheikh},
number = {11},
pages = {e1002683},
publisher = {Public Library of Science ({PLoS})},
url = {https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002683#pmed.1002683.s005},
volume = {15},
journal = {{PLOS} Medicine},
month = {nov}
}
@inproceedings{Beede2020,
author = {Emma Beede and Elizabeth Baylor and Fred Hersch and Anna Iurchenko and Lauren Wilcox and Paisan Ruamviboonsuk and Laura M. Vardoulakis},
booktitle = {{Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems}},
title = {{A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy}},
doi = {10.1145/3313831.3376718},
publisher = {ACM},
month = {apr},
year = {2020}
}
@article{Obermeyer2019,
author = {Ziad Obermeyer and Brian Powers and Christine Vogeli and Sendhil Mullainathan},
title = {{Dissecting racial bias in an algorithm used to manage the health of populations}},
doi = {10.1126/science.aax2342},
number = {6464},
pages = {447--453},
volume = {366},
journal = {Science},
month = {oct},
publisher = {American Association for the Advancement of Science ({AAAS})},
year = {2019}
}
@inproceedings{Caruana2015,
author = {Rich Caruana and Yin Lou and Johannes Gehrke and Paul Koch and Marc Sturm and Noemie Elhadad},
booktitle = {{Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}},
title = {{Intelligible Models for HealthCare}},
doi = {10.1145/2783258.2788613},
publisher = {ACM Press},
year = {2015}
}
@misc{Economist2020,
author = {{The Economist}},
date = {2020-06-13},
editor = {{The Economist}},
title = {{Artificial Intelligence and its Limits - Reality Check}},
year = {2020},
howpublished = {\href{https://www.economist.com/technology-quarterly/2020/06/11/an-understanding-of-ais-limitations-is-starting-to-sink-in}{Link to publication 2020-06-20}},
url = {https://www.economist.com/technology-quarterly/2020/06/11/an-understanding-of-ais-limitations-is-starting-to-sink-in},
urldate = {2020-06-20}
}
@misc{Schiffer2020,
author = {Zoe Schiffer},
date = {2020-01-24},
title = {{Aurora is finally ready to show the world what it’s been up to}},
year = {2020},
howpublished = {\href{https://www.theverge.com/2020/1/24/21080298/aurora-self-driving-car-announcement-2020-plan-waymo-ford-general-motors}{Link to publication 2020-06-21}},
subtitle = {{The secretive self-driving car company unveiled its plans for 2020}},
url = {https://www.theverge.com/2020/1/24/21080298/aurora-self-driving-car-announcement-2020-plan-waymo-ford-general-motors},
urldate = {2020-06-21}
}
@article{Nagendran2020,
author = {Myura Nagendran and Yang Chen and Christopher A. Lovejoy and Anthony C. Gordon and Matthieu Komorowski and Hugh Harvey and Eric J. Topol and John P. A. Ioannidis and Gary S. Collins and Mahiben Maruthappu},
title = {{Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies}},
doi = {10.1136/bmj.m689},
pages = {m689},
url = {https://www.bmj.com/content/368/bmj.m689},
journal = {BMJ},
month = {mar},
publisher = {BMJ},
year = {2020}
}
@misc{Verma2018,
author = {Sahil Verma and Julia Rubin},
title = {{Fairness definitions explained}},
doi = {10.1145/3194770.3194776},
howpublished = {\href{https://dl.acm.org/doi/10.1145/3194770.3194776}{Link to publicastion}},
url = {https://dl.acm.org/doi/10.1145/3194770.3194776},
urldate = {2020-08-07},
year = {2018}
}
@misc{Chiappa2019,
author = {Silvia Chiappa and William Isaac},
date = {2019-10-03},
title = {{Causal Bayesian Networks: A flexible tool to enable fairer machine learning}},
year = {2019},
howpublished = {\href{https://deepmind.com/blog/article/Causal_Bayesian_Networks}{Link to publication 2020-06-21}},
url = {https://deepmind.com/blog/article/Causal_Bayesian_Networks},
urldate = {2020-06-21}
}
@book{Hamon2020,
author = {Ronan Hamon},
title = {{Robustness and explainability of Artificial Intelligence : from technical to policy solutions}},
isbn = {9789276146605},
publisher = {Publications Office of the European Union},
address = {Luxembourg},
year = {2020}
}
@misc{Harvey,
author = {Hugh Harvey},
date = {2020-22-04},
title = {{Five shockingly simple questions to ask clinical AI vendors before you buy}},
year = {2020},
howpublished = {\href{https://hardianhealth.com/blog/5questions}{Link to publication 2020-06-23}},
url = {https://hardianhealth.com/blog/5questions},
urldate = {2020-06-23}
}
@inproceedings{Breck2017,
author = {Eric Breck and Shanqing Cai and Eric Nielsen and Michael Salib and D. Sculley},
booktitle = {{Proceedings of IEEE Big Data}},
title = {{The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction}},
year = {2017}
}
@inproceedings{Sculley2015,
author = {Sculley, D. and Holt, Gary and Golovin, Daniel and Davydov, Eugene and Phillips, Todd and Ebner, Dietmar and Chaudhary, Vinay and Young, Michael and Crespo, Jean-Francois and Dennison, Dan},
booktitle = {{Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2}},
title = {{Hidden Technical Debt in Machine Learning Systems}},
location = {Montreal, Canada},
pages = {2503–2511},
publisher = {MIT Press},
series = {NIPS’15},
url = {http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf},
urldate = {2020-08-22},
address = {Cambridge, MA, USA},
numpages = {9},
year = {2015}
}
@inproceedings{Amershi2019,
author = {Amershi, Saleema and Weld, Dan and Vorvoreanu, Mihaela and Fourney, Adam and Nushi, Besmira and Collisson, Penny and Suh, Jina and Iqbal, Shamsi and Bennett, Paul N. and Inkpen, Kori and Teevan, Jaime and Kikin-Gil, Ruth and Horvitz, Eric},
booktitle = {{Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems}},
title = {{Guidelines for Human-AI Interaction}},
doi = {10.1145/3290605.3300233},
isbn = {9781450359702},
location = {Glasgow, Scotland Uk},
pages = {1–13},
publisher = {Association for Computing Machinery},
series = {CHI ’19},
url = {https://doi.org/10.1145/3290605.3300233},
address = {New York, NY, USA},
keywords = {design guidelines, human-ai interaction, ai-infused systems},
numpages = {13},
year = {2019}
}
@article{Challen2019,
author = {Robert Challen and Joshua Denny and Martin Pitt and Luke Gompels and Tom Edwards and Krasimira Tsaneva-Atanasova},
title = {{Artificial intelligence, bias and clinical safety}},
doi = {10.1136/bmjqs-2018-008370},
number = {3},
pages = {231--237},
volume = {28},
journal = {{BMJ} Quality {\&} Safety},
month = {jan},
publisher = {{BMJ}},
year = {2019}
}
@Misc{Strickland2019,
author = {Eliza Strickland},
date = {2019-04-02},
title = {{How IBM Watson Overpromised and Underdelivered on AI Health Care}},
year = {2019},
howpublished = {\href{https://spectrum.ieee.org/biomedical/diagnostics/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-care\#LinkToAIHealthTable}{Link to publication 2020-06-24}},
note = {IEEE Spectrum},
url = {https://spectrum.ieee.org/biomedical/diagnostics/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-care#LinkToAIHealthTable},
urldate = {2020-06-24},
}
@misc{Harper2017,
author = {Matthew Harper},
date = {2017-02-17},
title = {{MD Anderson Benches IBM Watson In Setback For Artificial Intelligence In Medicine}},
year = {2017},
howpublished = {\href{https://www.forbes.com/sites/matthewherper/2017/02/19/md-anderson-benches-ibm-watson-in-setback-for-artificial-intelligence-in-medicine/}{Link to publication 2020-08-07}},
note = {Forbes},
url = {https://www.forbes.com/sites/matthewherper/2017/02/19/md-anderson-benches-ibm-watson-in-setback-for-artificial-intelligence-in-medicine/},
urldate = {2020-08-07}
}
@misc{TexasAdministration2017,
author = {{The University of Texas Administration}},
date = {2017-01},
title = {{Special Review of Procurement Procedures Related to the M.D. Anderson Cancer Center Oncology Expert Advisor Project}},
year = {2017},
howpublished = {\href{https://www.utsystem.edu/sites/default/files/documents/UT System Administration Special Review of Procurement Procedures Related to UTMDACC Oncology Expert Advisor Project/ut-system-administration-special-review-procurement-procedures-related-utmdacc-oncology-expert-advis.pdf}{Link to publication 2020-06-24}},
url = {https://www.utsystem.edu/sites/default/files/documents/UT System Administration Special Review of Procurement Procedures Related to UTMDACC Oncology Expert Advisor Project/ut-system-administration-special-review-procurement-procedures-related-utmdacc-oncology-expert-advis.pdf},
urldate = {2020-06-24}
}
@misc{Heaven2020,
author = {Will Douglas Heaven},
date = {2020-04-27},
title = {{Google’s medical AI was super accurate in a lab. Real life was a different story}},
year = {2020},
howpublished = {\href{https://www.technologyreview.com/2020/04/27/1000658/google-medical-ai-accurate-lab-real-life-clinic-covid-diabetes-retina-disease/}{Link to publication 2020-07-12}},
note = {MIT Technology Review},
url = {https://www.technologyreview.com/2020/04/27/1000658/google-medical-ai-accurate-lab-real-life-clinic-covid-diabetes-retina-disease/},
urldate = {2020-07-12}
}
@misc{Bourque2014,
author = {Pierre Bourque and Richard E. Fairley},
date = {2014},
title = {{Guide to the Software Engineering Body of Knowledge, Version 3.0}},
year = {2014},
howpublished = {\href{https://www.computer.org/education/bodies-of-knowledge/software-engineering}{Link to publication 2020-07-20}},
note = {IEEE Computer Society},
url = {https://www.computer.org/education/bodies-of-knowledge/software-engineering},
urldate = {2020-07-20}
}
@article{Haskins2004,
author = {Bill Haskins and Jonette Stecklein and Brandon Dick and Gregory Moroney and Randy Lovell and James Dabney},
title = {{Error Cost Escalation Through the Project Life Cycle}},
doi = {10.1002/j.2334-5837.2004.tb00608.x},
number = {1},
pages = {1723--1737},
volume = {14},
journal = {{INCOSE} International Symposium},
month = {jun},
publisher = {Wiley},
year = {2004}
}
@misc{Karpathy2017,
author = {Andrej Karpathy},
date = {2017-11-11},
title = {{Software 2.0}},
year = {2017},
howpublished = {\href{https://medium.com/@karpathy/software-2-0-a64152b37c35}{Link to publication 2020-07-17}},
url = {https://medium.com/@karpathy/software-2-0-a64152b37c35},
urldate = {2020-07-17}
}
@article{Degani1993,
author = {Asaf Degani and Earl L. Wiener},
title = {{Cockpit Checklists: Concepts, Design, and Use}},
doi = {10.1177/001872089303500209},
number = {2},
pages = {345--359},
volume = {35},
journal = {Human Factors: The Journal of the Human Factors and Ergonomics Society},
month = {jun},
publisher = {{SAGE} Publications},
year = {1993}
}
@article{Hersch2009,
author = {Matthew H. Hersch},
title = {{Checklist: The Secret Life of Apollo's "Fourth Crewmember"}},
doi = {10.1111/j.1467-954x.2009.01814.x},
number = {1{\_}suppl},
pages = {6--24},
volume = {57},
journal = {The Sociological Review},
month = {may},
publisher = {{SAGE} Publications},
year = {2009}
}
@misc{Hersch2009a,
author = {Matthew Hersch},
date = {2009-07-19},
editor = {{Air and Space Magazine}},
title = {{The Fourth Crewemember}},
year = {2009},
howpublished = {\href{https://www.airspacemag.com/space/the-fourth-crewmember-37046329/}{Link to publication 2020-07-22}},
note = {Air and Space Magazine},
url = {https://www.airspacemag.com/space/the-fourth-crewmember-37046329/},
urldate = {2020-07-22}
}
@book{Gawande2011,
author = {Gawande, Atul},
date = {2011-02-01},
title = {{The Checklist Manifesto}},
isbn = {0312430000},
pagetotal = {215},
publisher = {Macmillan USA},
url = {https://www.ebook.de/de/product/10051625/atul_gawande_the_checklist_manifesto.html},
ean = {9780312430009},
year = {2011}
}
@book{Institute2012,
author = {{Canadian Patient Safety Institute}},
date = {2012},
title = {{Canadian incident analysis framework}},
isbn = {9781926541457},
publisher = {Canadian Patient Safety Institute},
url = {https://www.patientsafetyinstitute.ca/en/toolsResources/IncidentAnalysis/Documents/Canadian Incident Analysis Framework.PDF},
urldate = {2020-07-22},
address = {Edmonton},
year = {2012}
}
@misc{Food2019,
author = {{Food and Drug Administration}},
date = {2019-04-01},
title = {{Code of Federal Regulations Title 21}},
year = {2019},
howpublished = {\href{https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm?cfrpart=820}{Link to publication 2020-07-22}},
url = {https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm?cfrpart=820},
urldate = {2020-07-22}
}
@article{Minssen2020,
author = {Timo Minssen and Sara Gerke and Mateo Aboy and Nicholson Price and Glenn Cohen},
title = {{Regulatory responses to medical machine learning}},
doi = {10.1093/jlb/lsaa002},
journal = {Journal of Law and the Biosciences},
month = {apr},
publisher = {Oxford University Press ({OUP})},
year = {2020}
}
@inproceedings{Raji2020,
author = {Inioluwa Deborah Raji and Andrew Smart and Rebecca N. White and Margaret Mitchell and Timnit Gebru and Ben Hutchinson and Jamila Smith-Loud and Daniel Theron and Parker Barnes},
booktitle = {{Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency}},
title = {{Closing the AI accountability gap defining an end-to-end framework for Internal algorithmic auditing v1}},
doi = {10.1145/3351095.3372873},
publisher = {{ACM}},
month = {jan},
year = {2020}
}
@inproceedings{Kulshrestha2017,
author = {Juhi Kulshrestha and Motahhare Eslami and Johnnatan Messias and Muhammad Bilal Zafar and Saptarshi Ghosh and Krishna P. Gummadi and Karrie Karahalios},
booktitle = {{Proceedings of the 2017 {ACM} Conference on Computer Supported Cooperative Work and Social Computing}},
title = {{Quantifying Search Bias}},
doi = {10.1145/2998181.2998321},
publisher = {{ACM}},
month = {feb},
year = {2017}
}
@article{Willemink2020,
author = {Martin J. Willemink and Wojciech A. Koszek and Cailin Hardell and Jie Wu and Dominik Fleischmann and Hugh Harvey and Les R. Folio and Ronald M. Summers and Daniel L. Rubin and Matthew P. Lungren},
title = {{Preparing Medical Imaging Data for Machine Learning}},
doi = {10.1148/radiol.2020192224},
number = {1},
pages = {4--15},
volume = {295},
journal = {Radiology},
month = {apr},
publisher = {Radiological Society of North America ({RSNA})},
year = {2020}
}
@article{Liu2019,
author = {Xiaoxuan Liu and Livia Faes and Aditya U. Kale and Siegfried K. Wagner and Dun Jack Fu and Alice Bruynseels and Thushika Mahendiran and Gabriella Moraes and Mohith Shamdas and Christoph Kern and Joseph R. Ledsam and Martin K. Schmid and Konstantinos Balaskas and Eric J.Topol and Lucas M. Bachmann and Pearse A. Keane and Alastair K. Denniston},
title = {{A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis}},
doi = {10.1016/s2589-7500(19)30123-2},
number = {6},
pages = {e271--e297},
volume = {1},
journal = {The Lancet Digital Health},
month = {oct},
publisher = {Elsevier {BV}},
year = {2019}
}
@article{Connolly2020,
author = {Randy Connolly},
title = {{Why computing belongs within the social sciences}},
doi = {10.1145/3383444},
number = {8},
pages = {54--59},
volume = {63},
journal = {Communications of the {ACM}},
month = {jul},
publisher = {Association for Computing Machinery ({ACM})},
year = {2020}
}
@article{Shiller2010,
author = {Robert J. Shiller},
title = {{How Should the Financial Crisis Change How We Teach Economics?}},
doi = {10.1080/00220485.2010.510409},
number = {4},
pages = {403--409},
volume = {41},
journal = {The Journal of Economic Education},
month = {sep},
publisher = {Informa {UK} Limited},
year = {2010}
}
@article{Hagendorff2020,
author = {Thilo Hagendorff},
title = {{The Ethics of AI Ethics: An Evaluation of Guidelines}},
doi = {10.1007/s11023-020-09517-8},
number = {1},
pages = {99--120},
volume = {30},
journal = {Minds and Machines},
month = {feb},
publisher = {Springer Science and Business Media {LLC}},
year = {2020}
}
@misc{JamesGuszcza2018,
author = {James Guszcza and Iyad Rahwan and Will Bible and Manuel Cebrian and Vic Katyal},
date = {2018-11-28},
title = {{Why we need to audit algorithms}},
year = {2018},
howpublished = {\href{https://hbr.org/2018/11/why-we-need-to-audit-algorithms}{Link to publication 2020-07-26}},
note = {Harvard Business Review},
url = {https://hbr.org/2018/11/why-we-need-to-audit-algorithms},
urldate = {2020-07-26}
}
@misc{Health2017,
author = {{National Insititutes of Health}},
date = {2017-09-27},
title = {{NIH Clinical Center provides one of the largest publicly available chest x-ray datasets to scientific community}},
year = {2017},
howpublished = {\href{https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community}{Link to publication 2020-07-27}},
url = {https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community},
urldate = {2020-07-27}
}
@inproceedings{Wang2017,
author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers},
booktitle = {{2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})}},
title = {{ChestX-ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases v5}},
doi = {10.1109/cvpr.2017.369},
publisher = {{IEEE}},
month = {jul},
year = {2017}
}
@misc{OakdenRayner2019,
author = {Luke Oakden-Rayner},
date = {2019-02-19},
title = {{Half a million x-rays! First impressions of the Stanford and MIT chest x-ray datasets}},
year = {2019},
howpublished = {\href{https://lukeoakdenrayner.wordpress.com/2019/02/25/half-a-million-x-rays-first-impressions-of-the-stanford-and-mit-chest-x-ray-datasets/}{Link to publication 2020-07-27}},
url = {https://lukeoakdenrayner.wordpress.com/2019/02/25/half-a-million-x-rays-first-impressions-of-the-stanford-and-mit-chest-x-ray-datasets/},
urldate = {2020-07-27}
}
@misc{OakdenRayner2017,
author = {Luke Oakden-Rayner},
date = {2017-11-18},
title = {{Quick thoughts on ChestXray14, performance claims, and clinical tasks}},
year = {2017},
howpublished = {\href{https://lukeoakdenrayner.wordpress.com/2017/11/18/quick-thoughts-on-chestxray14-performance-claims-and-clinical-tasks/}{Link to publication 2020-07-27}},
url = {https://lukeoakdenrayner.wordpress.com/2017/11/18/quick-thoughts-on-chestxray14-performance-claims-and-clinical-tasks/},
urldate = {2020-07-27}
}
@article{Baltruschat2019,
author = {Ivo M. Baltruschat and Hannes Nickisch and Michael Grass and Tobias Knopp and Axel Saalbach},
title = {{Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification}},
doi = {10.1038/s41598-019-42294-8},
number = {1},
volume = {9},
journal = {Scientific Reports},
month = {apr},
publisher = {Springer Science and Business Media {LLC}},
year = {2019}
}
@misc{OakdenRayner2018,
author = {Luke Oakden-Rayner},
date = {2018-12-17},
title = {{Exploring the ChestXray14 dataset: problems}},
year = {2018},
howpublished = {\href{https://lukeoakdenrayner.wordpress.com/2017/12/18/the-chestxray14-dataset-problems/}{Link to publication 2020-07-27}},
url = {https://lukeoakdenrayner.wordpress.com/2017/12/18/the-chestxray14-dataset-problems/},
urldate = {2020-07-27}
}
@article{Rajpurkar2017,
author = {Pranav Rajpurkar and Jeremy Irvin and Kaylie Zhu and Brandon Yang and Hershel Mehta and Tony Duan and Daisy Ding and Aarti Bagul and Curtis Langlotz and Katie Shpanskaya and Matthew P. Lungren and Andrew Y. Ng},
date = {2017-11-14},
title = {{CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning v3}},
year = {2017},
eprint = {1711.05225v3},
eprintclass = {cs.CV},
eprinttype = {arXiv},
abstract = {We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and achieve state of the art results on all 14 diseases.},
file = {:http\://arxiv.org/pdf/1711.05225v3:PDF},
keywords = {cs.CV, cs.LG, stat.ML}
}
@misc{Medicine2017,
author = {{Standford School of Medicine}},
date = {2017-11-15},
title = {{Algorithm better at diagnosing pneumonia than radiologists}},
year = {2017},
howpublished = {\href{http://med.stanford.edu/news/all-news/2017/11/algorithm-can-diagnose-pneumonia-better-than-radiologists.html}{Link to publication 2020-07-27}},
url = {http://med.stanford.edu/news/all-news/2017/11/algorithm-can-diagnose-pneumonia-better-than-radiologists.html},
urldate = {2020-07-27}
}
@misc{Radiology2017,
author = {{Stanford Radiology}},
date = {2017-11-15},
title = {{Stanford researchers develop algorithm that accurately diagnoses pneumonia}},
year = {2017},
howpublished = {\href{https://med.stanford.edu/radiology/news/2017/ai-research-diagnoses-pneumonia-better.html}{Link to publication 2020-07-27}},
url = {https://med.stanford.edu/radiology/news/2017/ai-research-diagnoses-pneumonia-better.html},
urldate = {2020-07-27}
}
@misc{OakdenRayner2018a,
author = {Luke Oakden-Rayner},
date = {2018-01-24},
title = {{CheXNet: an in-depth review}},
year = {2018},
howpublished = {\href{https://lukeoakdenrayner.wordpress.com/2018/01/24/chexnet-an-in-depth-review/}{Link to publication 2020-07-27}},
url = {https://lukeoakdenrayner.wordpress.com/2018/01/24/chexnet-an-in-depth-review/},
urldate = {2020-07-27}
}
@misc{Gebru2018,
author = {Timnit Gebru and Jamie Morgenstern and Briana Vecchione and Jennifer Wortman Vaughan and Hanna Wallach and Hal Daumé and Kate Crawford},
date = {2018-03-23},
title = {{Datasheets for Datasets v7}},
year = {2018},
eprint = {1803.09010v7},
eprintclass = {cs.DB},
eprinttype = {arXiv},
howpublished = {\href{https://arxiv.org/abs/1803.09010}{Link to publication 2020-08-06}},
url = {https://arxiv.org/abs/1803.09010},
urldate = {2020-08-08},
abstract = {The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose datasheets for datasets. In the electronics industry, every component, no matter how simple or complex, is accompanied with a datasheet that describes its operating characteristics, test results, recommended uses, and other information. By analogy, we propose that every dataset be accompanied with a datasheet that documents its motivation, composition, collection process, recommended uses, and so on. Datasheets for datasets will facilitate better communication between dataset creators and dataset consumers, and encourage the machine learning community to prioritize transparency and accountability.},
file = {:http\://arxiv.org/pdf/1803.09010v7:PDF},
keywords = {cs.DB, cs.AI, cs.LG}
}
@article{Mitchell2018,
author = {Margaret Mitchell and Simone Wu and Andrew Zaldivar and Parker Barnes and Lucy Vasserman and Ben Hutchinson and Elena Spitzer and Inioluwa Deborah Raji and Timnit Gebru},
date = {2018-10-05},
journaltitle = {{FAT* '19: Conference on Fairness, Accountability, and Transparency, January 29--31, 2019, Atlanta, GA, USA}},
title = {{Model Cards for Model Reporting v2}},
year = {2018},
doi = {10.1145/3287560.3287596},
eprint = {1810.03993v2},
eprintclass = {cs.LG},
eprinttype = {arXiv},
abstract = {Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related AI technology, increasing transparency into how well AI technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.},
file = {:http\://arxiv.org/pdf/1810.03993v2:PDF},
keywords = {cs.LG, cs.AI}
}
@misc{Topol2017,
author = {Eric Topol},
date = {2017-11-15},
title = {{The arXiv preprint CheXNet suggest, at best, matched 4 academic radiologists.}},
year = {2017},
howpublished = {\href{https://twitter.com/EricTopol/status/930980060835614720}{Link to publication 2020-07-27}},
url = {https://twitter.com/EricTopol/status/930980060835614720},
urldate = {2020-07-27}
}
@misc{Microsoft2020,
author = {Microsoft},
date = {2020-04-01},
title = {{What are Azure machine learning pipelines?}},
year = {2020},
howpublished = {\href{https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines}{Link to publication 2020-07-28}},
url = {https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines},
urldate = {2020-07-28}
}
@misc{Office2019,
author = {{Information Comminissioner's Office}},
date = {2019-03-26},
title = {{An overview of the auditing framework for artifical intelligence and its core components}},
year = {2019},
howpublished = {\href{https://ico.org.uk/about-the-ico/news-and-events/ai-blog-an-overview-of-the-auditing-framework-for-artificial-intelligence-and-its-core-components/}{Link to publication 2020-07-28}},
url = {https://ico.org.uk/about-the-ico/news-and-events/ai-blog-an-overview-of-the-auditing-framework-for-artificial-intelligence-and-its-core-components/},
urldate = {2020-07-28}
}
@conference{Cramer2019,
author = {H. Cramer and K. Holstein and J. Wortman Vaughan and H. Daumé and M. Dudík and H. Wallach and S. Reddy and J. Garcia-Gathright},
booktitle = {{ACM FAccT}},
date = {2019-01-29},
title = {{Challenges of incorporating algorithmic fairness into industry practice}},
url = {https://drive.google.com/file/d/1rUQkVS0NzSH3IEqZDsczSxBbhYHbjamN/view},
urldate = {2020-07-28},
year = {2019}
}
@misc{MerriamWebster,
author = {Merriam-Webster},
title = {{Transparent}},
year = {2020},
howpublished = {\href{https://www.merriam-webster.com/dictionary/transparent}{Link to publication 2020-07-28}},
url = {https://www.merriam-webster.com/dictionary/transparent},
urldate = {2020-07-28}
}
@article{Reyes2020,
author = {Mauricio Reyes and Raphael Meier and S{\'{e}}rgio Pereira and Carlos A. Silva and Fried-Michael Dahlweid and Hendrik von Tengg-Kobligk and Ronald M. Summers and Roland Wiest},
title = {{On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities}},
doi = {10.1148/ryai.2020190043},
number = {3},
pages = {e190043},
volume = {2},
journal = {Radiology: Artificial Intelligence},
month = {may},
publisher = {Radiological Society of North America ({RSNA})},
year = {2020}
}
@misc{Anderegg2020,
author = {Fabio Anderegg},
date = {2020},
title = {{Web-demo on interpretability of machine intelligence in medial image computing}},
year = {2020},
howpublished = {\href{http://imimic-workshop.com/demo.html}{Link to publication 2020-07-28}},
url = {http://imimic-workshop.com/demo.html},
urldate = {2020-07-28}
}
@misc{Eggers2019,
author = {William D. Eggers and Amrita Datar and Kevin Coltin},
date = {2019},
title = {{Government jobs of the future}},
year = {2019},
howpublished = {\href{https://www2.deloitte.com/content/dam/insights/us/articles/4767_FoW-in-govt/DI_Algorithm-auditor.pdf}{Link to publication 2020-07-28}},
note = {Deloittle Center for Government Insights},
url = {https://www2.deloitte.com/content/dam/insights/us/articles/4767_FoW-in-govt/DI_Algorithm-auditor.pdf},
urldate = {2020-07-28}
}
@misc{Google,
author = {Google},
title = {{Google Cloud Model Cards}},
year = {2020},
howpublished = {\href{https://modelcards.withgoogle.com/about}{Link to publication 2020-07-31}},
url = {https://modelcards.withgoogle.com/about},
urldate = {2020-07-31}
}
@misc{ThePartnership,
author = {{The Partnership on AI}},
title = {{ABOUT ML}},
year = {2020},
howpublished = {\href{https://www.partnershiponai.org/about-ml/}{Link to publication 2020-07-31}},
note = {The Partnership on AI},
url = {https://www.partnershiponai.org/about-ml/},
urldate = {2020-07-31}
}
@misc{Partneship2020,
author = {{The Partnership on AI}},
date = {2020},
title = {{Annotation and Benchmarking on Understanding and Transparency of Machine learning Lifecycles (ABOUT ML) chapter 2}},
year = {2020},
howpublished = {\href{https://www.partnershiponai.org/wp-content/uploads/2019/07/ABOUT-ML-v0-Draft-Chapter-2.pdf}{Link to publication 2020-08-01}},
url = {https://www.partnershiponai.org/wp-content/uploads/2019/07/ABOUT-ML-v0-Draft-Chapter-2.pdf},
urldate = {2020-08-01}
}
@inproceedings{Buolamwini2018,
author = {Buolamwini, Joy and Gebru, Timnit},
booktitle = {{Conference on fairness, accountability and transparency}},
title = {{Gender shades: Intersectional accuracy disparities in commercial gender classification}},
pages = {77--91},
year = {2018}
}
@article{Chan2020,
author = {Stephanie Chan and Vidhatha Reddy and Bridget Myers and Quinn Thibodeaux and Nicholas Brownstone and Wilson Liao},
title = {{Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations}},
doi = {10.1007/s13555-020-00372-0},
number = {3},
pages = {365--386},
volume = {10},
journal = {Dermatology and Therapy},
month = {apr},
publisher = {Springer Science and Business Media {LLC}},
year = {2020}
}
@misc{Jilani2020,
author = {Abdul Khader Jilani},
date = {2020-03-31},
title = {{Identifying Leakage in Computer Vision on Medical Images}},
year = {2020},
howpublished = {\href{https://www.datarobot.com/blog/identifying-leakage-in-computer-vision-on-medical-images/}{Link to publication 2020-08-02}},
url = {https://www.datarobot.com/blog/identifying-leakage-in-computer-vision-on-medical-images/},
urldate = {2020-08-02}
}
@book{Groopman2007,
author = {Jeremo Groopman},
date = {2007},
title = {{How Doctors Think}},
year = {2007},
chapter = {1},
isbn = {0-618-61003-0},
pages = {40},
publisher = {Mariner Books}
}
@misc{HealthClinicalCenter2017,
author = {{National Institutes of Health Clinical Center}},
date = {2017-09-01},
editor = {Ronald Summers},
title = {{ChestX-ray8}},
year = {2017},
howpublished = {\href{https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345}{Link to publication 2020-08-05}},
url = {https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345},
urldate = {2020-08-02}
}
@misc{HealthClinicalCenter,
author = {{National Institutes of Health Clinical Center}},
date = {2017-06-26},
title = {{Legal, Ethical, and Safety Issues}},
year = {2017},
howpublished = {\href{https://clinicalcenter.nih.gov/participate/patientinfo/legal1.html}{Link to publication 2020-08-05}},
url = {https://clinicalcenter.nih.gov/participate/patientinfo/legal1.html},
urldate = {2020-08-05}
}
@misc{Ste2019,
author = {Andrew Ste},
date = {2019-08},
title = {{How to Become More Marketable as a Data Scientist}},
year = {2019},
howpublished = {\href{https://www.kdnuggets.com/2019/08/marketable-data-scientist.html}{Link to publication 2020-08-05}},
url = {https://www.kdnuggets.com/2019/08/marketable-data-scientist.html},
urldate = {2020-08-05}
}
@article{Sendak2020,
author = {Mark P. Sendak and Michael Gao and Nathan Brajer and Suresh Balu},
title = {{Presenting machine learning model information to clinical end users with model facts labels}},
year = {2020},
doi = {10.1038/s41746-020-0253-3},
number = {1},
publisher = {Springer Science and Business Media {LLC}},
url = {https://www.nature.com/articles/s41746-020-0253-3},
urldate = {2020-08-23},
volume = {3},
journal = {npj Digital Medicine},
month = {mar}
}
@article{IEC2006,
author = {{International Eletrotechnical Commission}},
title = {{62304: 2006 Medical device software--software life cycle processes}},
url = {https://www.iso.org/standard/38421.html},
journal = {International Electrotechnical Commission, Geneva},
year = {2006}
}
@article{Goldstein2016,
author = {Benjamin A. Goldstein and Nrupen A. Bhavsar and Matthew Phelan and Michael J. Pencina},
title = {{Controlling for Informed Presence Bias Due to the Number of Health Encounters in an Electronic Health Record}},
doi = {10.1093/aje/kww112},
number = {11},
pages = {847--855},
url = {https://pubmed.ncbi.nlm.nih.gov/27852603/},
volume = {184},
journal = {American Journal of Epidemiology},
month = {nov},
publisher = {Oxford University Press ({OUP})},
year = {2016}
}
@article{Phelan2017,
author = {Phelan, Matthew and Bhavsar, Nrupen and Goldstein, Benjamin A.},
title = {{Illustrating Informed Presence Bias in Electronic Health Records Data: How Patient Interactions with a Health System Can Impact Inference}},
doi = {10.5334/egems.243},
number = {1},
pages = {22},
volume = {5},
journal = {{eGEMs} (Generating Evidence {\&} Methods to improve patient outcomes)},
month = {dec},
publisher = {Ubiquity Press, Ltd.},
year = {2017}
}
@article{Martensson2020,
author = {Gustav M{\aa}rtensson and Daniel Ferreira and Tobias Granberg and Lena Cavallin and Ketil Oppedal and Alessandro Padovani and Irena Rektorova and Laura Bonanni and Matteo Pardini and Milica G Kramberger and John-Paul Taylor and Jakub Hort and J{\'{o}}n Sn{\ae}dal and Jaime Kulisevsky and Frederic Blanc and Angelo Antonini and Patrizia Mecocci and Bruno Vellas and Magda Tsolaki and Iwona K{\l}oszewska and Hilkka Soininen and Simon Lovestone and Andrew Simmons and Dag Aarsland and Eric Westman},
title = {{The reliability of a deep learning model in clinical out-of-distribution {MRI} data: a multicohort study}},
doi = {10.1016/j.media.2020.101714},
pages = {101714},
journal = {Medical Image Analysis},
month = {may},
publisher = {Elsevier {BV}},
year = {2020}
}
@article{Pooch2019,
author = {Eduardo H. P. Pooch and Pedro L. Ballester and Rodrigo C. Barros},
date = {2019-09-03},
title = {{Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification}},
year = {2019},
eprint = {1909.01940v2},
eprintclass = {eess.IV},
eprinttype = {arXiv},
abstract = {While deep learning models become more widespread, their ability to handle unseen data and generalize for any scenario is yet to be challenged. In medical imaging, there is a high heterogeneity of distributions among images based on the equipment that generates them and their parametrization. This heterogeneity triggers a common issue in machine learning called domain shift, which represents the difference between the training data distribution and the distribution of where a model is employed. A high domain shift tends to implicate in a poor generalization performance from the models. In this work, we evaluate the extent of domain shift on four of the largest datasets of chest radiographs. We show how training and testing with different datasets (e.g., training in ChestX-ray14 and testing in CheXpert) drastically affects model performance, posing a big question over the reliability of deep learning models trained on public datasets. We also show that models trained on CheXpert and MIMIC-CXR generalize better to other datasets.},
file = {:http\://arxiv.org/pdf/1909.01940v2:PDF},
keywords = {eess.IV, cs.AI, cs.CV, cs.LG, stat.ML}
}
@article{Rajpurkar2018,
author = {Pranav Rajpurkar and Jeremy Irvin and Robyn L. Ball and Kaylie Zhu and Brandon Yang and Hershel Mehta and Tony Duan and Daisy Ding and Aarti Bagul and Curtis P. Langlotz and Bhavik N. Patel and Kristen W. Yeom and Katie Shpanskaya and Francis G. Blankenberg and Jayne Seekins and Timothy J. Amrhein and David A. Mong and Safwan S. Halabi and Evan J. Zucker and Andrew Y. Ng and Matthew P. Lungren},
title = {{Deep learning for chest radiograph diagnosis: A retrospective comparison of the {CheXNeXt} algorithm to practicing radiologists}},
doi = {10.1371/journal.pmed.1002686},
editor = {Aziz Sheikh},
number = {11},
pages = {e1002686},
volume = {15},
journal = {{PLOS} Medicine},
month = {nov},
publisher = {Public Library of Science ({PLoS})},
year = {2018}
}
@article{Sun2018,
author = {Youcheng Sun and Xiaowei Huang and Daniel Kroening and James Sharp and Matthew Hill and Rob Ashmore},
date = {2018-03-10},
title = {{Testing Deep Neural Networks}},
year = {2018},
eprint = {1803.04792v4},
eprintclass = {cs.LG},
eprinttype = {arXiv},
abstract = {Deep neural networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety-critical domains. However, traditional software test coverage metrics cannot be applied directly to DNNs. In this paper, inspired by the MC/DC coverage criterion, we propose a family of four novel test criteria that are tailored to structural features of DNNs and their semantics. We validate the criteria by demonstrating that the generated test inputs guided via our proposed coverage criteria are able to capture undesired behaviours in a DNN. Test cases are generated using a symbolic approach and a gradient-based heuristic search. By comparing them with existing methods, we show that our criteria achieve a balance between their ability to find bugs (proxied using adversarial examples) and the computational cost of test case generation. Our experiments are conducted on state-of-the-art DNNs obtained using popular open source datasets, including MNIST, CIFAR-10 and ImageNet.},
file = {:http\://arxiv.org/pdf/1803.04792v4:PDF},
keywords = {cs.LG, cs.CV, cs.SE}
}
@inproceedings{Amershi2019a,
author = {Amershi, Saleema and Begel, Andrew and Bird, Christian and DeLine, Robert and Gall, Harald and Kamar, Ece and Nagappan, Nachiappan and Nushi, Besmira and Zimmermann, Thomas},
booktitle = {{2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)}},
title = {{Software engineering for machine learning: A case study}},
organization = {IEEE},
pages = {291--300},
year = {2019}
}
@article{Cai2019,
author = {Carrie J. Cai and Samantha Winter and David Steiner and Lauren Wilcox and Michael Terry},
title = {{"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making}},
year = {2019},