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Breast cancer is the most common cancers and early detection from mammography screening is crucial in improving patient outcomes.
Breast imaging reporting and data system (bi-rads)
Liberman, L. and Menell, J. H · 2002
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Rajpurkar, P., Joshi, A., Pareek, A., Chen, P., Kiani, A., Irvin, J., Ng, A. Y., and Lungren, M. P · 2002
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Chexclusion: Fairness gaps in deep chest x-ray classifiers
Seyyed-Kalantari, L., Liu, G., McDermott, M. B. A., and Ghassemi, M · 2003
Earlier work this paper cites.
Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening
Pisano, E. D., Gatsonis, C., Hendrick, E., Yaffe, M., Baum, J. K., Acharyya, S., Conant, E. F., Fajardo, L. L., Bassett, L., D’Orsi, C., Jong, R., and Rebner, M · 2005
Earlier work this paper cites.
Algorithmic Learning in a Random World
Vovk, V., Gammerman, A., and Shafer, G · 2005
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Conformal prediction with neural networks
Papadopoulos, H., Vovk, V., and Gammerman, A · 2007
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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
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Mammographic density and breast cancer risk: Current understanding and future prospects
Boyd, N., Martin, L., Yaffe, M., and Minkin, S · 2011
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Distribution-free prediction sets
Lei, J., Robins, J., and Wasserman, L · 2012
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Mammographic breast density: Impact on breast cancer risk and implications for screening
Freer, P. E · 2015
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Racial Differences in Quantitative Measures of Area and Volumetric Breast Density
McCarthy, A. M., Keller, B. M., Pantalone, L. M., Hsieh, M.-K., Synnestvedt, M., Conant, E. F., Armstrong, K., and Kontos, D · 2016
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Screening for breast cancer: Us preventive services task force recommendation statement
Siu, A. L. and Force, U. P. S. T · 2016
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Variation in mammographic breast density assessments among radiologists in clinical practice: a multicenter observational study
Sprague, B. L., Conant, E. F., Onega, T., Garcia, M. P., Beaber, E. F., Herschorn, S. D., Lehman, C. D., Tosteson, A. N., Lacson, R., Schnall, M. D., et al · 2016
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Discrimination of breast cancer with microcalcifications on mammography by deep learning
Wang, J., Yang, X., Cai, H., Tan, W. Y., Jin, C., and Li, L · 2016
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Selective classification for deep neural networks
Geifman, Y. and El-Yaniv, R · 2017
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Why cad failed in mammography
Kohli, A. and Jha, S · 2017
Cited alongside, same era.
Least ambiguous set-valued classifiers with bounded error levels
Sadinle, M., Lei, J., and Wasserman, L · 2017
Cited alongside, same era.
Predict responsibly: Improving fairness and accuracy by learning to defer
Madras, D., Pitassi, T., and Zemel, R · 2018
Cited alongside, same era.
Importance of better human-computer interaction in the era of deep learning: Mammography computer-aided diagnosis as a use case
Nishikawa, R. M. and Bae, K. T · 2018
Cited alongside, same era.
Breast density classification with deep convolutional neural networks
Wu, N., Geras, K. J., Shen, Y., Su, J., Kim, S. G., Kim, E., Wolfson, S., Moy, L., and Cho, K · 2018
Cited alongside, same era.
The need for uncertainty quantification in machine-assisted medical decision making
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
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International evaluation of an ai system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M. D., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., Ledsam, J. R., Melnick, D. S., Mostofi, H., Peng, L. H., Reicher, J. J., Romera-Paredes, B., Sidebottom, R., Suleyman, M., Tse, D., Young, K. C., Fauw, J. D., and Shetty, S · 2020
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The reliability of a deep learning model in clinical out-of-distribution mri data: A multicohort study
Mårtensson, G., Ferreira, D., Granberg, T., Cavallin, L., Oppedal, K., Padovani, A., Rektorova, I., Bonanni, L., Pardini, M., Kramberger, M. G., Taylor, J.-P., Hort, J., Snædal, J., Kulisevsky, J., Blanc, F., Antonini, A., Mecocci, P., Vellas, B., Tsolaki, M., Kłoszewska, I., Soininen, H., Lovestone, S., Simmons, A., Aarsland, D., and Westman, E · 2020
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Classification with valid and adaptive coverage
Romano, Y., Sesia, M., and Candès, E. J · 2020
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Begoli, E., Bhattacharya, T., and Kusnezov, D · 2019
Cited alongside, same era.
Improving workflow efficiency for mammography using machine learning
Kyono, T., Gilbert, F. J., and van der Schaar, M · 2019
Cited alongside, same era.
Mammographic breast density assessment using deep learning: Clinical implementation
Lehman, C. D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., and Barzilay, R · 2019
Cited alongside, same era.
Deep learning to improve breast cancer detection on screening mammography
Shen, L., Margolies, L., Rothstein, J., Fluder, E., McBride, R., and Sieh, W · 2019
Cited alongside, same era.
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J · 2019
Cited alongside, same era.
A deep learning mammography-based model for improved breast cancer risk prediction
Yala, A., Lehman, C., Schuster, T., Portnoi, T., and Barzilay, R · 2019
Cited alongside, same era.
Fairness in machine learning for healthcare
Ahmad, M. A., Patel, A., Eckert, C., Kumar, V., and Teredesai, A · 2020
Cited alongside, same era.
Association of Breast Density With Breast Cancer Risk Among Women Aged 65 Years or Older by Age Group and Body Mass Index
Advani, S. M., Zhu, W., Demb, J., Sprague, B. L., Onega, T., Henderson, L. M., Buist, D. S. M., Zhang, D., Schousboe, J. T., Walter, L. C., Kerlikowske, K., Miglioretti, D. L., Braithwaite, D., and Consortium, B. C. S · 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
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Algorithm fairness in ai for medicine and healthcare, 2021
Chen, R. J., Chen, T. Y., Lipkova, J., Wang, J. J., Williamson, D. F. K., Lu, M. Y., Sahai, S., and Mahmood, F · 2021
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Addressing catastrophic forgetting for medical domain expansion
Gupta, S., Singh, P., Chang, K., Qu, L., Aggarwal, M., Arun, N. T., Vaswani, A., Raghavan, S., Agarwal, V., Gidwani, M., Hoebel, K., Patel, J. B., Lu, C., Bridge, C. P., Rubin, D. L., and Kalpathy-Cramer, J · 2021
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Fair conformal predictors for applications in medical imaging, 2021
Lu, C., Lemay, A., Chang, K., Hoebel, K., and Kalpathy-Cramer, J · 2021
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Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Seyyed-Kalantari, L., Zhang, H., McDermott, M., Chen, I. Y., and Ghassemi, M · 2021
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Artificial intelligence sepsis prediction algorithm learns to say “i don’t know”
Shashikumar, S., Wardi, G., Malhotra, A., and Nemati, S · 2021
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Toward robust mammography-based models for breast cancer risk
Yala, A., Mikhael, P. G., Strand, F., Lin, G., Smith, K., Wan, Y.-L., Lamb, L., Hughes, K., Lehman, C., and Barzilay, R · 2021
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Breast cancer—epidemiology, risk factors, classification, prognostic markers, and current treatment strategies—an updated review
Łukasiewicz, S., Czeczelewski, M., Forma, A., Baj, J., Sitarz, R., and Stanisławek, A · 2021
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Ai recognition of patient race in medical imaging: a modelling study
Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L.-C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.-C., et al · 2022
Closest in time.
Conformal prediction in clinical medical sciences
Vazquez, J. and Facelli, J · 2022
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