See also Google Scholar

Peer reviewed journal articles

S. Lee, D. C. Elton, J. L. Gulley, P. J. Pickhardt, W. L. Dahut, R. A. Madan, P. A. Pinto, D. E. Citrin, R. M. Summers. “Association of Abdominal Calcified Atherosclerotic Plaque with Prostate Cancer: A Case-Control Study” (under review)

D. C. Elton. “Applying Deutsch’s concept of good explanations to artificial intelligence and neuroscience - an initial exploration”. (under review )

P. J. Pickhardt, P. M. Graffy, A. A. Perez, M. G. Lubner, D. C. Elton, R. M. Summers. “Opportunistic Screening at Abdominal CT using Automated Biomarkers: Adding Value Beyond the Clinical Indication” to appear in Radiographics. (2020)

P. J. Pickhardt, G. Blake, P. M. Graffy, V. Sandfort, D. C. Elton, A. A. Perez, R. M. Summers. “Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully-Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1,204 Heathy Adults Using Unenhanced CT as Reference Standard”. American Journal of Roentgenology. (2020)

P. J. Pickhardt, D. C. Elton, P. M. Graffy, S. J. Lee, J. Liu, V. Sandfort, R. M. Summers. “Fully-automated CT Imaging Biomarkers of Bone, Muscle, and Fat: Correcting for the Effect of Intravenous Contrast”. Abdominal Radiology (2021)

R. M. Summers, D. C. Elton, S. Lee, Y. Zhu, J. Liu, M. Bagheri, V. Sanfort, P. C. Grayson, N. N. Mehta, P. A. Pinto, W. M. Linehan, A. A. Perez, P. M. Graffy, S. O’Connor, P. J. Pickhardt. “Atherosclerotic Plaque Burden on Abdominal CT: Automated Assessment with Deep Learning on Noncontrast and Contrast-enhanced Scans”. Academic Radiology (2020)

D. C. Elton, P. D. Spencer, J. D. Riches, E. D. Williams. “Exclusion zone phenomena in water - a critical review of experimental findings and theoriesInternational Journal of Molecular Sciences, 21 (14), 5041 (2020) [arXiv]

D. C. Elton, Z. Boukouvalas, M. D. Fuge, and P. W. Chung, “Deep learning for molecular design - a review of the state of the art”, Molecular Systems Design & Engineering, 4 (2019) [full pdf]

G. Kumar, F. G. VanGessel, D. C. Elton, and P. W. Chung. “Phonon Lifetimes and Thermal Conductivity of the Molecular Crystal α-RDX”, MRS Advances, 4, 2191 (2019) [arXiv]

D. C. Elton, M. Fritz, and M.-V Fernández-Serra. “Using a monomer potential energy surface to perform approximate path integral molecular dynamics simulation of ab-initio water at near-zero added cost”, Phys. Chem. Chem. Phys., 21, 409 (2019) [arXiv]

D. C. Elton, Z. Boukouvalas, M. S. Butrico, M. D. Fuge, and P. W. Chung, “Applying machine learning techniques to predict the properties of energetic materials”, Scientific Reports, 8, 9059 (2018).

D.C. EltonThe microscopic origin of the Debye relaxation in liquid water and fitting the high frequency excess response”, Phys. Chem. Chem. Phys., 19, 18739 (2017) [arXiv]

D.C. Elton and M.-V Fernández-Serra. “The hydrogen bond network of water supports propagating optical phonon-like modes”, Nat. Comm. 7, 10913 (2016)

D.C. Elton and M.-V Fernández-Serra. “Polar nanoregions in water – a study of the dielectric properties of TIP4P/2005,TIP4P/2005f and TTM3F”, J. Chem. Phys., 140, 124504 (2014) [arXiv]

J. J. Podesta, M. A. Forman, C. W. Smith, D. C. Elton, and Y. Malecot, “Accurate Estimation of Third-Order Moments from Turbulence Measurements“, Nonlin. Proc. Geophys., 16, 99 (2009) [arXiv]

Peer reviewed conference proceedings

D. C. Elton, Y. Zhu, Y. Tang, R. M. Summers. “Improving the Transferability of 3D Segmentation Models Using Cycle Consistent Adversarial Networks” (in prep)

Y. Peng, S. Lee, D. C. Elton, T. Shen, Y. Tang, Q. Chen, S. Wang, Y. Zhu, R. M. Summers, Z. Lu. “Automatic Recognition of Lymph Nodes from Clinical Text”. to appear in Proceedings of the 3rd Workshop on Clinical Natural Language Processing (ClinicalNLP) (2020)

S. Y. Shin, S. Lee, D. C. Elton, J. Gulley, R. M. Summers. “Deep Small Bowel Segmentation with Cylindrical Topological Constraints”. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, edited by Anne L. Martel et al., Springer International Publishing, 12264, 207–15. (2020) [arxiv]

Y. Zhu, Y. Tang, Y. Tang, D. C. Elton, S. Lee, P. J. Pickhardt, R. M. Summers. “Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model”. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, edited by Anne L. Martel et al., Springer International Publishing, 12262, 379–389. (2020) [arXiv]

D. C. Elton. “Self-explaining AI as an alternative to interpretable AI”. Proceedings of the 13th Annual Conference on Artificial General Intelligence (AGI-2020), pg 95. [arXiv] (Won the 2020 Kurzweil Prize for Best AGI Idea)

Z. Boukouvalas, M. Puerto, D. C. Elton, P. W. Chung, M. D. Fuge. “Independent Vector Analysis for Molecular Data Fusion: Application to Property Prediction and Knowledge Discovery of Energetic Materials”. Proceedings of the 28th European Signal Processing Conference (EUSIPCO 2020). (2020)

Y. Zhu, D. C. Elton, S. Lee, P. J. Pickhardt, R. M. Summers. “Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Detection”. Proceedings of the 2020 Medical Imaging with Deep Learning (MIDL) Conference. (2020). [arXiv]

D. C. Elton, V. Sandfort, P. J. Pickhardt, and R. M. Summers. “Accurately identifying vertebral levels in large datasets”. SPIE: Medical Imaging 2020: Computer-Aided Diagnosis 113140O. (2020). [arXiv]

D. C. Elton, D. Turakhia, N. Reddy, Z. Boukouvalas, R. M. Doherty, M. D. Fuge, and P. W. Chung. “Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora”. Proceedings of the 22nd International Seminar on New Trends in Research of Energetic Materials. (2019). [arXiv] (won the 3rd best presentation award at the 2019 NTREM conference)

Z. Boukouvalas, D. C. Elton, M. D. Fuge, and P. W. Chung. “Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning”. Proceedings of the 2018 Neural Information Processing Systems (NeurIPS) workshop on Machine Learning for Molecules and Materials. [arXiv]

B. C. Barnes, D. C. Elton, Z. Boukouvalas, D. E. Taylor, W. D. Mattson, M. D. Fuge, and P.W. Chung, “Machine Learning of Energetic Material Properties”, Proceedings of the 16th International Detonation Symposium, Cambridge MD, USA, July 2018, [arXiv]

F. G. VanGessel, G. Kumar, D. C. Elton, and P. W. Chung, “A Phonon Boltzmann Study of Microscale Thermal Transport in α-RDX Cook-Off”, Proceedings of the 16th International Detonation Symposium, Cambridge MD, USA, July 2018. [arXiv]

M. A. Forman, C. W. Smith, B. J. Vasquez, B. T. MacBride, J. E. Stawarz, J. J. Podesta, D. C. Elton, U. Y. Malecot, and Y. Gagne. “Using Third‐Order Moments of Fluctuations in V and B to Determine Turbulent Heating Rates in the Solar Wind”, AIP Conference Proceedings 1216, 12th International Solar Wind Conference, 176 (2010)

Book chapters

D. C. Elton and P. D. Spencer. “Four examples of pathological water science and what they have in common”. To appear in Water in Biomechanical & Related Systems”, A. Gadomski, editor. Springer. (2021)

Preprints

C. S. Greene, D. C. Elton, A. J. Titus, A. Gitter, D. S. Himmelstein, B. C. Christensen, J. J. Levy, et al. “Opportunities and obstacles for deep learning in biology and medicine: Version 2.0” (in prep) (2020)

Ph.D. Thesis

atom in a clathrate-like cage

Understanding the Dielectric Properties of Water (11 Mb PDF)

Science notes

feedback on these is always appreciated.