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Interpretability in machine learning models is important in high-stakes decisions, such as whether to order a biopsy based on a mammographic exam.
An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers
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Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
Elizabeth R DeLong, David M DeLong, and Daniel L Clarke-Pearson · 1988
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Robert T Macura and Katarzyna J Macura · 1995
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Breast imaging reporting and data system standardized mammography lexicon: Observer variability in lesion description
Jay A Baker, Phyllis J Kornguth, and CE Floyd Jr · 1996
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Bi-rads categorization as a predictor of malignancy
Susan G Orel, Nicole Kay, Carol Reynolds, and Daniel C Sullivan · 1999
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Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions
Carey E Floyd Jr, Joseph Y Lo, and Georgia D Tourassi · 2000
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A case-based training system in radiology-senology
Souad Demigha and Nicolas Prat · 2004
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Computer-aided diagnosis of intracranial aneurysms in mra images with case-based reasoning
Syoji Kobashi, Katsuya Kondo, and Yutaka Hata · 2006
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Bi-rads lexicon for us and mammography: interobserver variability and positive predictive value
Elizabeth Lazarus, Martha B Mainiero, Barbara Schepps, Susan L Koelliker, and Linda S Livingston · 2006
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The prediction of breast cancer biopsy outcomes using two cad approaches that both emphasize an intelligible decision process
Matthias Elter, Rüdiger Schulz-Wendtland, and Thomas Wittenberg · 2007
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Observer agreement using the acr breast imaging reporting and data system (bi-rads)-ultrasound, (2003)
Chang Suk Park, Jae Hee Lee, Hyeon Woo Yim, Bong Joo Kang, Hyeon Sook Kim, Jung Im Jung, Na Young Jung, and Sung Hun Kim · 2007
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Anniversary paper: history and status of cad and quantitative image analysis: the role of medical physics and aapm
Maryellen L Giger, Heang-Ping Chan, and John Boone · 2008
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Breast imaging reporting and data system lexicon for us: interobserver agreement for assessment of breast masses
Nouf Abdullah, Benoît Mesurolle, Mona El-Khoury, and Ellen Kao · 2009
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Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings
Elizabeth S Burnside, Jesse Davis, Jagpreet Chhatwal, Oguzhan Alagoz, Mary J Lindstrom, Berta M Geller, Benjamin Littenberg, Katherine A Shaffer, Charles E Kahn Jr, and C David Page · 2009
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Optimal breast biopsy decision-making based on mammographic features and demographic factors
Jagpreet Chhatwal, Oguzhan Alagoz, and Elizabeth S Burnside · 2010
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Evaluation of clinical breast mr imaging performed with prototype computer-aided diagnosis breast mr imaging workstation: reader study
Akiko Shimauchi, Maryellen L Giger, Neha Bhooshan, Li Lan, Lorenzo L Pesce, John K Lee, Hiroyuki Abe, and Gillian M Newstead · 2011
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Acr bi-rads® mammography
EA Sickles, CJ d’Orsi, LW Bassett, CM Appleton, WA Berg, ES Burnside, et al · 2013
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A convolutional neural network for modelling sentences
Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom · 2014
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Fast implementation of delong’s algorithm for comparing the areas under correlated receiver operating characteristic curves
Xu Sun and Weichao Xu · 2014
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External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets
Matthias Benndorf, Elizabeth S Burnside, Christoph Herda, Mathias Langer, and Elmar Kotter · 2015
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Breast imaging reporting and data system (bi-rads) lexicon for breast mri: interobserver variability in the description and assignment of bi-rads category
Mona El Khoury, Lucie Lalonde, Julie David, Maude Labelle, Benoit Mesurolle, and Isabelle Trop · 2015
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Diagnostic accuracy of digital screening mammography with and without computer-aided detection
Constance D Lehman, Robert D Wellman, Diana SM Buist, Karla Kerlikowske, Anna NA Tosteson, and Diana L Miglioretti · 2015
Cited alongside, same era.
Very Deep Convolutional Networks for Large-Scale Image Recognition
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan K Su, and Cynthia Rudin · 2019
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A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound: Added value for the inexperienced breast radiologist
Hee Jeong Park, Sun Mi Kim, Bo La Yun, Mijung Jang, Bohyoung Kim, Ja Yoon Jang, Jong Yoon Lee, and Soo Hyun Lee · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Convolutional neural networks for radiologic images: a radiologist’s guide
Shelly Soffer, Avi Ben-Cohen, Orit Shimon, Michal Marianne Amitai, Hayit Greenspan, and Eyal Klang · 2019
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Removing confounding factors associated weights in deep neural networks improves the prediction accuracy for healthcare applications
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Karen Simonyan and Andrew Zisserman · 2015
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A data augmentation methodology for training machine/deep learning gait recognition algorithms
Christoforos Charalambous and Anil Bharath · 2016
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Robust arbitrary view gait recognition based on parametric 3d human body reconstruction and virtual posture synthesis
Jian Luo, Jin Tang, Tardi Tjahjadi, and Xiaoming Xiao · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Context augmentation for convolutional neural networks
Aysegul Dundar and Ignacio Garcia-Dorado · 2017
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Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-grained Image Recognition
Jianlong Fu, Heliang Zheng, and Tao Mei · 2017
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Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition
Heliang Zheng, Jianlong Fu, Tao Mei, and Jiebo Luo · 2017
Cited alongside, same era.
Haohan Wang, Zhenglin Wu, and Eric P Xing · 2019
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Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition
Julia K Winkler, Christine Fink, Ferdinand Toberer, Alexander Enk, Teresa Deinlein, Rainer Hofmann-Wellenhof, Luc Thomas, Aimilios Lallas, Andreas Blum, Wilhelm Stolz, et al · 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, Stanislaw Jastrzebski, Thibault Fevry, Joe Katsnelson, Eric Kim, et al · 2019
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Towards interpretable object detection by unfolding latent structures
Tianfu Wu and Xi Song · 2019
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Assessing the (un) trustworthiness of saliency maps for localizing abnormalities in medical imaging
Nishanth Arun, Nathan Gaw, Praveer Singh, Ken Chang, Mehak Aggarwal, Bryan Chen, Katharina Hoebel, Sharut Gupta, Jay Patel, Mishka Gidwani, et al · 2020
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Concept whitening for interpretable image recognition
Zhi Chen, Yijie Bei, and Cynthia Rudin · 2020
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Stratified rule-aware network for abstract visual reasoning
Sheng Hu, Yuqing Ma, Xianglong Liu, Yanlu Wei, and Shihao Bai · 2020
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Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study
Hyo-Eun Kim, Hak Hee Kim, Boo-Kyung Han, Ki Hwan Kim, Kyunghwa Han, Hyeonseob Nam, Eun Hye Lee, and Eun-Kyung Kim · 2020
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Mortality in the united states, 2019
Kenneth D. Kochanek, Jiaquan Xu, and Elizabeth Arias · 2020
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International evaluation of an ai system for breast cancer screening
Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg C Corrado, Ara Darzi, et al · 2020
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External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms
Mattie Salim, Erik Wåhlin, Karin Dembrower, Edward Azavedo, Theodoros Foukakis, Yue Liu, Kevin Smith, Martin Eklund, and Fredrik Strand · 2020
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Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms
Thomas Schaffter, Diana SM Buist, Christoph I Lee, Yaroslav Nikulin, Dezső Ribli, Yuanfang Guan, William Lotter, Zequn Jie, Hao Du, Sijia Wang, et al · 2020
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, and Kristian Kersting · 2020
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Mitigating gender bias in captioning systems
Ruixiang Tang, Mengnan Du, Yuening Li, Zirui Liu, and Xia Hu · 2020
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Noise or signal: The role of image backgrounds in object recognition
Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
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Training confounder-free deep learning models for medical applications
Qingyu Zhao, Ehsan Adeli, and Kilian M Pohl · 2020
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