[19] Hokuto Hirano, Akinori Minagi, and Kazuhiro Takemoto. Universal adversarial attacks on deep neural networks for medical image classification. BMC medical imaging, 21:1-13, 2021.
[20] Chaowei Xiao, Dawei Yang, Bo Li, Jia Deng, and Mingyan Liu. Meshadv: Adversarial meshes for visual recognition. In CVPR, pages 6898-6907, 2019.
[21] Xiaohui Zeng, Chenxi Liu, Yu-Siang Wang, Weichao Qiu, Lingxi Xie, Yu-Wing Tai, Chi-Keung Tang, and Alan L Yuille. Adversarial attacks beyond the image space. In CVPR, pages 4302-4311, 2019.
[22] Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, and Alec Jacobson. Beyond pixel norm-balls: Parametric adversaries using an analytically differentiable renderer. arXiv preprint arXiv:1808.02651, 2018.
[23] Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, et al. 3db: A framework for debugging computer vision models. arXiv preprint arXiv:2106.03805, 2021.
[24] Aldo Badano, Christian G Graff, Andreu Badal, Diksha Sharma, Rongping Zeng, Frank W Samuelson, Stephen J Glick, and Kyle J Myers. Evaluation of digital breast tomosynthesis as replacement of full-field digital mammography using an in silico imaging trial. JAMA network open, 1(7):e185474-e185474, 11 2018.
[25] Steve A. Maas, Benjamin J. Ellis, Gerard A. Ateshian, and Jeffrey A. Weiss. FEBio: Finite Elements for Biomechanics. Journal of Biomechanical Engineering, 134(1):011005, 02 2012.
[26] Luis de Sisternes, Jovan G Brankov, Adam M Zysk, Robert A Schmidt, Robert M Nishikawa, and Miles N Wernick. A computational model to generate simulated three-dimensional breast masses. Medical physics, 42(2):1098-1118, 2015.
[27] Andrea Kim, Aunnasha Sengupta, and Aldo Badano. Automated animation pipeline for visualizing in silico tumor growth models. Physics of Medical Imaging, 12463, 2023.
[28] Aunnasha Sengupta, Andreu Badal, Andrey Makeev, and Aldo Badano. Computational models of direct and indirect x-ray breast imaging detectors for in silico trials. Medical Physics, 49(11):6856-6870, 2022.
[29] Helen ML Frazer, Jennifer SN Tang, Michael S Elliott, Katrina M Kunicki, Brendan Hill, Ravishankar Karthik, Chun Fung Kwok, Carlos A Peña-Solorzano, Yuanhong Chen, Chong Wang, et al. Admani: Annotated digital mammograms and associated non-image datasets. Radiology: Artificial Intelligence, 5(2):e220072, 2022.
[30] Chunyan Cui, Li Li, Hongmin Cai, Zhihao Fan, Ling Zhang, Tingting Dan, Jiao Li, and Jinghua Wang. The chinese mammography database (cmmd): An online mammography database with biopsy confirmed types for machine diagnosis of breast. Data Cancer Imaging Arch, 2021.
[31] Mark D. Halling-Brown, Lucy M. Warren, Dominic Ward, Emma Lewis, Alistair Mackenzie, Matthew G. Wallis, Louise S. Wilkinson, Rosalind M. Given-Wilson, Rita McAvinchey, and Kenneth C. Young. Optimam mammography image database: A large-scale resource of mammography images and clinical data. Radiology: Artificial Intelligence, 3(1), 2020.
[32] Inês C Moreira, Igor Amaral, Inês Domingues, António Cardoso, Maria Joao Cardoso, and Jaime S Cardoso. Inbreast: toward a full-field digital mammographic database. Academic radiology, 19(2):236-248, 2012.
[33] Mateusz Buda, Ashirbani Saha, Ruth Walsh, Sujata Ghate, Nianyi Li, Albert Świecicki, Joseph Y Lo, and Maciej A Mazurowski. Detction of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model. arXiv preprint arXiv:2011.07995, 2020.
[34] Jiwoong J Jeong, Brianna L Vey, Ananth Bhimireddy, Thomas Kim, Thiago Santos, Ramon Correa, Raman Dutt, Marina Mosunjac, Gabriela Oprea-Ilies, Geoffrey Smith, et al. The emory breast imaging dataset (embed): A racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images. Radiology: Artificial Intelligence, 5(1):e220047, 2023.
[35] Berkman Sahiner. Digital mammography dream challenge overview (conference presentation). In Medical Imaging 2017: Computer-Aided Diagnosis, volume 10134, pages 1159-1159. SPIE, 2017.
[36] Antonio Sarno, Giovanni Mettivier, Francesca di Franco, Antonio Varallo, Kristina Bliznakova, Andrew M. Hernandez, John M. Boone, and Paolo Russo. Dataset of patient-derived digital breast phantoms for in silico studies in breast computed tomography, digital breast tomosynthesis, and digital mammography. Medical Physics, 48(5):2682-2693, 2021.