UNSURE2023 special issue

https://doi.org/10.59275/j.melba.2025-f9a8

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

Guest editors,
  • Christian Baumgartner, University of Lucerne;
  • Adrian Dalca, Massachusetts General Hospital, Harvard Medical School, Massachusetts Institute of Technology;
  • Raghav Mehta, Imperial College London;
  • Chen Qin, Imperial College London;
  • Carole Sudre, University College London;
  • William (Sandy) Wells, Massachusetts Institute of Technology, Brigham and Women's Hospital, Boston.
Workshop page

2 papers


Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases cover file

Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases

2025/01/15
UNSURE2023 special issue

Jacob J. PeoplesSchool of Computing, Queen’s University, Kingston, ON, Canada et al.

Jacob J. PeoplesSchool of Computing, Queen’s University, Kingston, ON, Canada, Mohammad HamghalamSchool of Computing, Queen’s University, Kingston, ON, Canada
Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran, Imani JamesDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA, Maida WasimDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA, Natalie GangaiDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA, Hyunseon Christine KangDepartment of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, X. John RongDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, Yun Shin ChunDepartment of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, Richard K. G. DoDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA, Amber L. SimpsonSchool of Computing, Queen’s University, Kingston, ON, Canada
Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada

PDF · Code · arXiv
Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation cover file

Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation

2024/10/23
UNSURE2023 special issue

McKell WoodlandThe University of Texas MD Anderson Cancer Center, Houston, TX, USA
Rice University, Houston, TX, USA et al.

McKell WoodlandThe University of Texas MD Anderson Cancer Center, Houston, TX, USA
Rice University, Houston, TX, USA, Nihil PatelThe University of Texas MD Anderson Cancer Center, Houston, TX, USA, Austin CasteloThe University of Texas MD Anderson Cancer Center, Houston, TX, USA, Mais Al TaieThe University of Texas MD Anderson Cancer Center, Houston, TX, USA, Mohamed EltaherThe University of Texas MD Anderson Cancer Center, Houston, TX, USA, Joshua P. YungThe University of Texas MD Anderson Cancer Center, Houston, TX, USA, Tucker J. NethertonThe University of Texas MD Anderson Cancer Center, Houston, TX, USA, Tiffany L. CalderoneThe University of Texas MD Anderson Cancer Center, Houston, TX, USA, Jessica I. SanchezThe University of Texas MD Anderson Cancer Center, Houston, TX, USA, Darrel W. CleereHouston Methodist Hospital, Houston, TX, USA, Ahmed ElsaieyHouston Methodist Hospital, Houston, TX, USA, Nakul GuptaHouston Methodist Hospital, Houston, TX, USA, David VictorHouston Methodist Hospital, Houston, TX, USA, Laura BerettaThe University of Texas MD Anderson Cancer Center, Houston, TX, USA, Ankit B. PatelBaylor College of Medicine, Houston, TX, USA
Rice University, Houston, TX, USA, Kristy K. BrockThe University of Texas MD Anderson Cancer Center, Houston, TX, USA

PDF · Code · Video · arXiv