Nick C. Konz

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Email: nicholas (dot) konz (at) duke (dot) edu

I’m a Ph.D. candidate studying machine learning at Duke University under Prof. Maciej Mazurowski. My research is primarily in deep learning for medical image analysis, in topics such as domain adaptation, image translation and generation, and anomaly detection. Additionally, I am drawn to understanding deep learning and medical image analysis from a scientific and foundational perspective. I have also worked as a research intern in the Math, Stats, and Data Science Group at PNNL.

See my Google Scholar page for a full list of my publications, with a few recent papers highlighted in the section below.

My undergraduate degree was a double major in physics and mathematics at the University of North Carolina at Chapel Hill, where my research with Prof. Daniel Reichart was on statistical techniques for astronomical data applications. I have been an educator in machine learning, physics, math and astronomy in the academic setting and beyond.

Selected Recent Papers

  1. The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images
    Nicholas Konz, and Maciej A Mazurowski
    In The Twelfth International Conference on Learning Representations (ICLR), 2024
    arXiv
  2. Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
    Nicholas Konz, Yuwen Chen, Haoyu Dong, and 1 more author
    arXiv preprint, 2024
    arXiv
  3. Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation
    Haoyu Dong, Nicholas Konz, Hanxue Gu, and 1 more author
    In Conference on Computer Vision and Pattern Recognition (CVPR) 2024: Workshop on Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis, 2024
    arXiv
  4. Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion
    Nicholas Konz, Haoyu Dong, and Maciej A Mazurowski
    Medical Image Analysis, 2023
    arXiv
  5. Attributing Learned Concepts in Neural Networks to Training Data
    Nicholas Konz, Charles Godfrey, Madelyn Shapiro, and 3 more authors
    In Advances in Neural Information Processing Systems (NeurIPS): Workshop on Attributing Model Behavior at Scale (Oral), 2023
    arXiv