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Explainability in Automated Ground Glass Opacity (GGO) Segmentation Using Chest CT Scans

  • Shereen Fouad
  • , Paula Atim
  • , Arvind Rajasekaran
  • , Pankaj Nagori
  • , John Morlese
  • , Bahadar Bhatia
  • school of computer science and digital engineering, Aston University
  • Sandwell and West Birmingham Hospitals NHS Trust

Research output: Unpublished contribution to conference › Abstract › peer-review

20 Downloads (Pure)
Original languageEnglish
Publication statusPublished - 14 Apr 2025
EventInternational Symposium on Biomedical Imaging - Hyatt Regency, Houston, United States
Duration: 14 Apr 2025 → 17 Apr 2025
https://biomedicalimaging.org/2025/

Conference

ConferenceInternational Symposium on Biomedical Imaging
Abbreviated titleISBI 2025
Country/TerritoryUnited States
CityHouston
Period14/04/25 → 17/04/25
Internet address

Bibliographical note

This is the author's accepted manuscript of an abstract presented at the 2025 International Symposium on Biomedical Imaging.

Access to Document

    • 1 Conference publication

    • Explainable Deep Learning Framework for ground glass opacity (GGO) Segmentation from Chest CT scans

      Atim, P., Fouad , S., Tiffany Yu, S., Fratini, A., Rajasekaran, A., Nagori, P., Morlese, J. & Bhatia, B., 19 Nov 2024, (E-pub ahead of print) Proceedings of 5th International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024). Springer

      Research output: Chapter in Book/Published conference output › Conference publication

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