1 code implementation • EMNLP (ClinicalNLP) 2020 • Yifan Peng, SungWon Lee, Daniel C. Elton, Thomas Shen, Yu-Xing Tang, Qingyu Chen, Shuai Wang, Yingying Zhu, Ronald Summers, Zhiyong Lu
We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports.
no code implementations • 9 Nov 2021 • Tejas Sudharshan Mathai, SungWon Lee, Daniel C. Elton, Thomas C. Shen, Yifan Peng, Zhiyong Lu, Ronald M. Summers
Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases.
no code implementations • 2 Oct 2021 • Bruce Nielson, Daniel C. Elton
Moreover, at a more meta level the process of development of all AI algorithms can be understood under the framework of universal Darwinism.
no code implementations • 16 Dec 2020 • Daniel C. Elton
We argue that figuring out how replicate this second system, which is capable of generating hard-to-vary explanations, is a key challenge which needs to be solved in order to realize artificial general intelligence.
no code implementations • 16 Jul 2020 • Seung Yeon Shin, Sung-Won Lee, Daniel C. Elton, James L. Gulley, Ronald M. Summers
Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation.
no code implementations • 14 Jul 2020 • Yingying Zhu, You-Bao Tang, Yu-Xing Tang, Daniel C. Elton, Sung-Won Lee, Perry J. Pickhardt, Ronald M. Summers
We expect the utility of our framework will extend to other problems beyond segmentation due to the improved quality of the generated images and enhanced ability to preserve small structures.
no code implementations • 22 May 2020 • Yingying Zhu, Daniel C. Elton, SungWon Lee, Perry J. Pickhardt, Ronald M. Summers
In medical imaging applications, preserving small structures is important since these structures can carry information which is highly relevant for disease diagnosis.
no code implementations • 12 Feb 2020 • Daniel C. Elton
While it is often possible to approximate the input-output relations of deep neural networks with a few human-understandable rules, the discovery of the double descent phenomena suggests that such approximations do not accurately capture the mechanism by which deep neural networks work.
no code implementations • 28 Jan 2020 • Daniel C. Elton, Veit Sandfort, Perry J. Pickhardt, Ronald M. Summers
We next developed an algorithm which performs iterative instance segmentation and classification of the entire spine with a 3D U-Net.
no code implementations • MIDL 2019 • Yingying Zhu, Daniel C. Elton, SungWon Lee, Perry J. Pickhardt, Ronald M. Summers
In medical imaging applications, preserving small structures is important since these structures can carry information which is highly relevant for disease diagnosis.
1 code implementation • 26 Apr 2019 • Gaurav Kumar, Francis G. VanGessel, Daniel C. Elton, Peter W. Chung
This is likely because diffusive carriers contribute to over 95% of the thermal conductivity in ${\alpha}$-RDX.
Materials Science
no code implementations • 11 Mar 2019 • Daniel C. Elton, Zois Boukouvalas, Mark D. Fuge, Peter W. Chung
In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text.
no code implementations • 1 Mar 2019 • Daniel C. Elton, Dhruv Turakhia, Nischal Reddy, Zois Boukouvalas, Mark D. Fuge, Ruth M. Doherty, Peter W. Chung
The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information and actionable insights from the latest research is becoming a considerable challenge.
1 code implementation • 1 Nov 2018 • Zois Boukouvalas, Daniel C. Elton, Peter W. Chung, Mark D. Fuge
Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery.
1 code implementation • 24 Aug 2018 • Francis G. VanGessel, Gaurav Kumar, Daniel C. Elton, Peter W. Chung
The microscale thermal transport properties of $\alpha$RDX are believed to be major factors in the initiation process.
Materials Science
2 code implementations • 17 Jul 2018 • Brian C. Barnes, Daniel C. Elton, Zois Boukouvalas, DeCarlos E. Taylor, William D. Mattson, Mark D. Fuge, Peter W. Chung
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure.
Materials Science Chemical Physics Computational Physics
no code implementations • 15 Mar 2018 • Daniel C. Elton, Michelle Fritz, M. -V. Fernández-Serra
We show that our method, which we call "monomer PIMD", correctly captures changes in the structure of water found in a full PIMD simulation but at much lower computational cost.
Chemical Physics Soft Condensed Matter Computational Physics