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Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset.
The meaning and use of the area under a receiver operating characteristic (roc) curve
James A Hanley and Barbara J McNeil · 1982
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The digital database for screening mammography
K Bowyer, D Kopans, WP Kegelmeyer, R Moore, M Sallam, K Chang, and K Woods · 1996
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Current status of the digital database for screening mammography
Michael Heath, Kevin Bowyer, Daniel Kopans, P Kegelmeyer, Richard Moore, Kyong Chang, and S Munishkumaran · 1998
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Auc optimization vs. error rate minimization
Corinna Cortes and Mehryar Mohri · 2004
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Optimising area under the roc curve using gradient descent
Alan Herschtal and Bhavani Raskutti · 2004
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A support vector method for multivariate performance measures
Thorsten Joachims · 2005
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Fast objective and duality gap convergence for non-convex strongly-concave min-max problems
Zhishuai Guo, Zhuoning Yuan, Yan Yan, and Tianbao Yang · 2006
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Melanoma
Arlo J Miller and Martin C Mihm Jr · 2006
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Asirra: a captcha that exploits interest-aligned manual image categorization
Jeremy Elson, John R Douceur, Jon Howell, and Jared Saul · 2007
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Ranking and empirical minimization of u-statistics
Stephan Clemencon, Gabor Lugosi, and Nicolas Vayatis · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Online auc maximization
Peilin Zhao, Steven C. H. Hoi, Rong Jin, and Tianbao Yang · 2011
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One-pass auc optimization
Wei Gao, Rong Jin, Shenghuo Zhu, and Zhi-Hua Zhou · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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On the consistency of auc pairwise optimization
Wei Gao and Zhi-Hua Zhou · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alex Alemi · 2016
Cited alongside, same era.
Stochastic online auc maximization
Yiming Ying, Longyin Wen, and Siwei Lyu · 2016
Cited alongside, same era.
Chexpert: A large chest x-ray dataset and competition
Stanford ML Group · 2019
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al · 2019
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Stochastic auc maximization with deep neural networks
Mingrui Liu, Zhuoning Yuan, Yiming Ying, and Tianbao Yang · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
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Deep neural networks improve radiologists’ performance in breast cancer screening
Nan Wu, Jason Phang, Jungkyu Park, Yiqiu Shen, Zhe Huang, Masha Zorin, Stanisław Jastrzkebski, Thibault Févry, Joe Katsnelson, Eric Kim, et al · 2019
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Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, et al · 2017
Cited alongside, same era.
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Cited alongside, same era.
Cyclical learning rates for training neural networks
Leslie N Smith · 2017
Cited alongside, same era.
Maximizing auc with deep learning for classification of imbalanced mammogram datasets
Jeremias Sulam, Rami Ben-Ari, and Pavel Kisilev · 2017
Cited alongside, same era.
Fast stochastic auc maximization with o ( 1 / n ) o(1/n) -convergence rate
Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, and Tianbao Yang · 2018
Cited alongside, same era.
Later among the works it cites.
https://www.isic-archive.com/
The international skin imaging collaboration (isic) · 2020
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Triple stratified kfold with tfrecords
Chris Deotte · 2020
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Qishen Ha, Bo Liu, and Fuxu Liu · 2020
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Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2020
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Interpreting chest x-rays via cnns that exploit hierarchical disease dependencies and uncertainty labels
Hieu H. Pham, Tung T. Le, Dat T. Ngo, Dat Q. Tran, and Ha Q. Nguyen · 2020
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A patient-centric dataset of images and metadata for identifying melanomas using clinical context
Veronica Rotemberg, Nicholas Kurtansky, Brigid Betz-Stablein, Liam Caffery, Emmanouil Chousakos, Noel Codella, Marc Combalia, Stephen Dusza, Pascale Guitera, David Gutman, et al · 2020
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Weakly supervised lesion localization with probabilistic-cam pooling, 2020
Wenwu Ye, Jin Yao, Hui Xue, and Yi Li · 2020
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Federated deep auc maximization for hetergeneous data with a constant communication complexity
Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, and Tianbao Yang · 2021
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