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Deep-LIBRA: Artificial intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment.

O. Maghsoudi, A. Gastounioti, C. Scott, L. Pantalone, F. Wu, E. Cohen, S. Winham, E. Conant, C. Vachon, and D. Kontos. CoRR, (2020)

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Robust radiomic feature selection in digital mammography: understanding the effect of imaging acquisition physics using phantom and clinical data analysis.R. Acciavatti, E. Cohen, O. Maghsoudi, A. Gastounioti, L. Pantalone, M. Hsieh, E. Conant, C. Scott, S. Winham, K. Kerlikowske and 3 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 11314 of SPIE Proceedings, SPIE, (2020)A Novel Computerized Tool to Stratify Risk in Carotid Atherosclerosis Using Kinematic Features of the Arterial Wall.A. Gastounioti, S. Makrodimitris, S. Golemati, N. Kadoglou, C. Liapis, and K. Nikita. IEEE J. Biomed. Health Informatics, 19 (3): 1137-1145 (2015)GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging.S. Pati, S. Thakur, M. Bhalerao, U. Baid, C. Grenko, B. Edwards, M. Sheller, J. Agraz, B. Baheti, V. Bashyam and 8 other author(s). CoRR, (2021)Validation of the textural realism of a 3D anthropomorphic phantom for digital breast tomosynthesis.R. Acciavatti, M. Hsieh, A. Gastounioti, Y. Hu, J. Chen, A. Maidment, and D. Kontos. IWBI, volume 10718 of SPIE Proceedings, page 107180R. SPIE, (2018)Parenchymal texture measures weighted by breast anatomy: preliminary optimization in a case-control study.A. Gastounioti, B. Keller, M. Hsieh, E. Conant, and D. Kontos. Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 97850M. SPIE, (2016)Volumetric versus area-based density assessment: comparisons using automated quantitative measurements from a large screening cohort.A. Gastounioti, M. Hsieh, L. Pantalone, E. Conant, and D. Kontos. Medical Imaging: Image Processing, volume 10574 of SPIE Proceedings, page 105742H. SPIE, (2018)Deep-LIBRA: Artificial intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment.O. Maghsoudi, A. Gastounioti, C. Scott, L. Pantalone, F. Wu, E. Cohen, S. Winham, E. Conant, C. Vachon, and D. Kontos. CoRR, (2020)Affine optical flow combined with multiscale image analysis for motion estimation of the arterial wall from B-mode ultrasound.A. Gastounioti, N. Tsiaparas, S. Golemati, J. Stoitsis, and K. Nikita. EMBC, page 559-562. IEEE, (2011)Convolutional neural network approach for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk.A. Oustimov, A. Gastounioti, M. Hsieh, L. Pantalone, E. Conant, and D. Kontos. Medical Imaging: Computer-Aided Diagnosis, volume 10134 of SPIE Proceedings, page 101340S. SPIE, (2017)Performance Gaps of Artificial Intelligence Models Screening Mammography - Towards Fair and Interpretable Models.L. Zhang, B. Brown-Mulry, V. Nalla, I. Hwang, J. Gichoya, A. Gastounioti, I. Banerjee, L. Seyyed-Kalantari, M. Woo, and H. Trivedi. CoRR, (2023)

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Deep-LIBRA: Artificial intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment.

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