Data science, AI, and machine learning in physics II

ORAL · Z18 · ID: 2155868

Presentations

  • Longitudinal Interpretability of Deep-Learning based Breast Cancer Risk Prediction Model

    ORAL

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    Presenters

    • Brayden Schott

      University of Wisconsin - Madison

    Authors

    • Zan Klanecek

      University of Ljubljana, Faculty of Mathematics and Physics, Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia, University of Ljubljana, Faculty of Mathematics and Physics, Medical Physics, Ljubljana, Slovenia

    • Yao Kuan Wang

      KU Leuven, Department of Imaging and Pathology, Division of Medical Physics & Quality Assessment, Leuven, Belgium

    • Tobias Wagner

      KU Leuven, Department of Imaging and Pathology, Division of Medical Physics & Quality Assessment, Leuven, Belgium

    • Lesley Cockmartin

      UZ Leuven, Department of Radiology, Leuven, Belgium

    • Nicholas Marshall

      KU Leuven, Department of Imaging and Pathology, Division of Medical Physics & Quality Assessment, Leuven, Belgium and UZ Leuven, Department of Radiology, Leuven, Belgium

    • Brayden Schott

      University of Wisconsin - Madison

    • Alison Deatsch

      University of Wisconsin - Madison

    • Andrej Studen

      Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia; Experimental Particle Physics Department, Jožef Stefan Institute, Ljubljana, Slovenia, University of Ljubljana, Faculty of Mathematics and Physics, Medical Physics, Ljubljana, Slovenia and Jožef Stefan Institute, Ljubljana, Slovenia

    • Miloš Vrhovec

      Institute of Oncology Ljubljana, Ljubljana, Slovenia

    • Hilde Bosmans

      KU Leuven, Department of Imaging and Pathology, Division of Medical Physics & Quality Assessment, Leuven, Belgium and UZ Leuven, Department of Radiology, Leuven, Belgium

    • Robert Jeraj

      University of Ljubljana, Faculty of Mathematics and Physics, Slovenia; Jožef Stefan Institute, Ljubljana, Slovenia; University of Wisconsin - Madison, USA, University of Ljubljana, Faculty of Mathematics and Physics, Slovenia and Jožef Stefan Institute, Slovenia and University of Wisconsin-Madison, Madison, U.S.A.

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  • Assessing the impact of CNN architectures for whole organ segmentation on predictive models of organ toxicity

    ORAL

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    Presenters

    • Katja Strasek

      Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia

    Authors

    • Katja Strasek

      Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia

    • Daniel Huff

      Department of Medical Physics, University of Wisconsin - Madison

    • Nežka Hribernik

      Department of Medical Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia

    • Victor S Fernandes

      University of Wisconsin - Madison, Department of Medical Physics, University of Wisconsin - Madison

    • Vincent T Ma

      University of Wisconsin Carbone Cancer Center, Madison, WI; Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI

    • Zan Klanecek

      University of Ljubljana, Faculty of Mathematics and Physics, Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia, University of Ljubljana, Faculty of Mathematics and Physics, Medical Physics, Ljubljana, Slovenia

    • Andrej Studen

      Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia; Experimental Particle Physics Department, Jožef Stefan Institute, Ljubljana, Slovenia, University of Ljubljana, Faculty of Mathematics and Physics, Medical Physics, Ljubljana, Slovenia and Jožef Stefan Institute, Ljubljana, Slovenia

    • Katarina Zevnik

      Department of Nuclear Medicine, Institute of Oncology Ljubljana, Ljubljana, Slovenia

    • Martina Reberšek

      Department of Medical Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia

    • Robert Jeraj

      University of Ljubljana, Faculty of Mathematics and Physics, Slovenia; Jožef Stefan Institute, Ljubljana, Slovenia; University of Wisconsin - Madison, USA, University of Ljubljana, Faculty of Mathematics and Physics, Slovenia and Jožef Stefan Institute, Slovenia and University of Wisconsin-Madison, Madison, U.S.A.

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  • Deep learning for image analysis of breast and prostate cancer cell cultures

    ORAL

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    Publication: 1. O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, in MICCAI 2015, pp. 234–241 (2015)
    2. O. Oktay et al., Attention U-Net: Learning Where to Look for the Pancreas, ArXiv 1804:03999 (2018)

    Presenters

    • Aliakbar Sepehri

      University of North Dakota

    Authors

    • Aliakbar Sepehri

      University of North Dakota

    • Ian Bergerson

      University of North Dakota

    • Yen Lee Loh

      University of North Dakota

    • Lucas Bierscheid

      North dakota university state

    • John Wilkinson

      North Dakota State University

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  • Structure prediction of iron hydrides across pressure range with transferable machine-learned interatomic potential

    ORAL

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    Publication: Hossein Tahmasbi, Kushal Ramakrishna, Mani Lokamani, and Attila Cangi "Machine Learning-Driven Structure Prediction for Iron Hydrides", In preparation

    Presenters

    • Hossein Tahmasbi

      Center for Advanced Systems Understanding (CASUS), HZDR

    Authors

    • Hossein Tahmasbi

      Center for Advanced Systems Understanding (CASUS), HZDR

    • Kushal Ramakrishna

      Helmholtz Zentrum Dresden-Rossendorf

    • Mani Lokamani

      Helmholtz-Zentrum Dresden-Rossendorf

    • Attila Cangi

      Helmholtz Zentrum Dresden-Rossendorf

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  • Development of a Machine Learning Interatomic Potential for Uranium Nitride

    ORAL

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    Presenters

    • Lorena Alzate-Vargas

      Los Alamos National Laboratory

    Authors

    • Lorena Alzate-Vargas

      Los Alamos National Laboratory

    • Richard A Messerly

      Los Alamos National Laboratory

    • Roxanne M Tutchton

      Los Alamos National Laboratory

    • Kashi N Subedi

      Los Alamos National Laboratory

    • Michael Cooper

      Los Alamos National Laboratory

    • Tammie Gibson

      Los Alamos National Laboratory

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  • Uncertainty Quantification for Deep Learning-based Metastatic Tumor Delineation on <sup>68</sup>Ga-DOTATATE PET/CT Images

    ORAL

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    Presenters

    • Brayden Schott

      University of Wisconsin - Madison

    Authors

    • Brayden Schott

      University of Wisconsin - Madison

    • Victor S Fernandes

      University of Wisconsin - Madison, Department of Medical Physics, University of Wisconsin - Madison

    • Zan Klanecek

      University of Ljubljana, Faculty of Mathematics and Physics, Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia, University of Ljubljana, Faculty of Mathematics and Physics, Medical Physics, Ljubljana, Slovenia

    • Dmitry Pinchuk

      University of Wisconsin - Madison

    • Robert Jeraj

      University of Wisconsin - Madison

    View abstract →