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Deep learning has the potential to automate many clinically useful tasks in medical imaging.
Algorithmic Learning in a Random World , chapter 2, 17–51
Vovk, V.; Gammerman, A.; and Shafer, G. 2005 · 2005
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Uncertainty in Deep Learning
Gal, Y. 2016 · 2016
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Gal, Y.; and Ghahramani, Z. 2016 · 2016
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Aggregated Residual Transformations for Deep Neural Networks
Xie, S.; Girshick, R. B.; Dollár, P.; Tu, Z.; and He, K. 2016 · 2016
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On Calibration of Modern Neural Networks
Guo, C.; Pleiss, G.; Sun, Y.; and Weinberger, K. Q. 2017 · 2017
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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Hendrycks, D.; and Gimpel, K. 2017 · 2017
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Inherent Trade-Offs in the Fair Determination of Risk Scores
Kleinberg, J.; Mullainathan, S.; and Raghavan, M. 2017 · 2017
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Counterfactual Fairness
Kusner, M. J.; Loftus, J.; Russell, C.; and Silva, R. 2017 · 2017
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Lakshminarayanan, B.; Pritzel, A.; and Blundell, C. 2017 · 2017
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Fairness Beyond Disparate Treatment and Disparate Impact
Zafar, M. B.; Valera, I.; Gomez Rodriguez, M.; and Gummadi, K. P. 2017 · 2017
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Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Buolamwini, J.; and Gebru, T. 2018 · 2018
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The need for uncertainty quantification in machine-assisted medical decision making
Begoli, E.; Bhattacharya, T.; and Kusnezov, D. 2019 · 2019
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Adversarial attacks on medical machine learning
Finlayson, S. G.; Bowers, J. D.; Ito, J.; Zittrain, J. L.; Beam, A. L.; and Kohane, I. S. 2019 · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Kopf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
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Conformalized Quantile Regression
Romano, Y.; Patterson, E.; and Candes, E. 2019 · 2019
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IBM Watson, heal thyself: How IBM overpromised and underdelivered on AI health care
Strickland, E. 2019 · 2019
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High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. 2019 · 2019
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CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis
Xie, Y.; Chen, M.; Kao, D.; Gao, G.; and Chen, X. A. 2020 · 2020
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Uncertainty sets for image classifiers using conformal prediction
Angelopoulos, A. N.; Bates, S.; Malik, J.; and Jordan, M. I. 2021 · 2021
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Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging
Arun, N.; Gaw, N.; Singh, P.; Chang, K.; Aggarwal, M.; Chen, B.; Hoebel, K.; Gupta, S.; Patel, J.; Gidwani, M.; Adebayo, J.; Li, M. D.; and Kalpathy-Cramer, J. 2021 · 2021
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Reading Race: AI Recognises Patient’s Racial Identity In Medical Images
Banerjee, I.; Bhimireddy, A. R.; Burns, J. L.; Celi, L. A.; Chen, L.-C.; Correa, R.; Dullerud, N.; Ghassemi, M.; Huang, S.-C.; Kuo, P.-C.; et al. 2021 · 2021
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Fairness and Machine Learning
Barocas, S.; Hardt, M.; and Narayanan, A. 2019 · 2021
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Yang, Q.; Steinfeld, A.; and Zimmerman, J. 2019 · 2019
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2020 ACR Data Science Institute Artificial Intelligence Survey
Allen, B.; Agarwal, S.; Coombs, L. P.; Dreyer, K.; and Wald, C. 2021 · 2020
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A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy , 1–12
Beede, E.; Baylor, E.; Hersch, F.; Iurchenko, A.; Wilcox, L.; Ruamviboonsuk, P.; and Vardoulakis, L. M. 2020 · 2020
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Debiasing Skin Lesion Datasets and Models? Not So Fast
Bissoto, A.; Valle, E.; and Avila, S. 2020 · 2020
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Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
Larrazabal, A. J.; Nieto, N.; Peterson, V.; Milone, D. H.; and Ferrante, E. 2020 · 2020
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Deep Conformal Prediction for Robust Models
Messoudi, S.; Rousseau, S.; and Destercke, S. 2020 · 2020
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With Malice Toward None: Assessing Uncertainty via Equalized Coverage
Romano, Y.; Barber, R. F.; Sabatti, C.; and Candès, E. 2020 · 2020
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Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty , 401–413
Bhatt, U.; Antorán, J.; Zhang, Y.; Liao, Q. V.; Sattigeri, P.; Fogliato, R.; Melançon, G.; Krishnan, R.; Stanley, J.; Tickoo, O.; Nachman, L.; Chunara, R.; Srikumar, M.; Weller, A.; and Xiang, A. 2021 · 2021
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Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction
Cauchois, M.; Gupta, S.; and Duchi, J. C. 2021 · 2021
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Artificial Intelligence and Machine Learning (AI/ML) Software as a Medical Device Action Plan
Food, U.; and Administration, D. 2021 · 2021
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Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset
Groh, M.; Harris, C.; Soenksen, L.; Lau, F.; Han, R.; Kim, A.; Koochek, A.; and Badri, O. 2021 · 2021
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Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens
Jacobs, M.; He, J.; Pradier, M. F.; Lam, B.; Ahn, A. C.; McCoy, T. H.; Perlis, R. H.; Doshi-Velez, F.; and Gajos, K. Z. 2021 · 2021
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Second opinion needed: communicating uncertainty in medical machine learning
Kompa, B.; Snoek, J.; and Beam, A. 2021 · 2021
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The AI Index 2021 Annual Report
Zhang, D.; Mishra, S.; Brynjolfsson, E.; Etchemendy, J.; Ganguli, D.; Grosz, B.; Lyons, T.; Manyika, J.; Niebles, J. C.; Sellitto, M.; Shoham, Y.; Clark, J.; and Perrault, R. 2021 · 2021
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Conformal Prediction in Clinical Medical Sciences
Vazquez, J.; and Facelli, J. 2022 · 2022
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