Back to the list
Congress: ECR25
Poster Number: C-11877
Type: Poster: EPOS Radiologist (scientific)
Authorblock: I. A. Selby1, A. Breger2, M. Roberts1, L. Escudero Sanchez1, J. Babar1, F. J. Gilbert1, C-B. Schönlieb1, E. Sala3, J. R. Weir-Mccall4; 1Cambridge/UK, 2Vienna/AT, 3Rome/IT, 4London/UK
Disclosures:
Ian Andrew Selby: Nothing to disclose
Anna Breger: Nothing to disclose
Michael Roberts: Nothing to disclose
Lorena Escudero Sanchez: Nothing to disclose
Judith Babar: Nothing to disclose
Fiona J. Gilbert: Nothing to disclose
Carola-Bibiane Schönlieb: Nothing to disclose
Evis Sala: Nothing to disclose
Jonathan R. Weir-Mccall: Nothing to disclose
Keywords: Artificial Intelligence, Lung, Thorax, Digital radiography, Plain radiographic studies, Computer Applications-General, Quality assurance
References
  1. Cushnan D, Bennett O, Berka R, Bertolli O, Chopra A, Dorgham S, et al. An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis. Gigascience. 2021 Nov;10(11). doi: 10.1093/gigascience/giab076
  2. Vayá M de la I, Saborit JM, Montell JA, Pertusa A, Bustos A, Cazorla M, et al. BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients. ArXiv. 2020 Jun. doi: 10.48550/arXiv.2006.01174
  3. Signoroni A, Savardi M, Benini S, Adami N, Leonardi R, Gibellini P, et al. BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset. Med Image Anal. 2021 Jul;71:102046. doi: 10.1016/j.media.2021.102046
  4. Tsai EB, Simpson S, Lungren MP, Hershman M, Roshkovan L, Colak E, et al. The RSNA International COVID-19 Open Radiology Database (RICORD). Radiology [Internet]. 2021 Jan 5 [cited 2021 Aug 15];299(1):E204–13. doi: 10.1148/radiol.2021203957
  5. Cambridge University Hospitals (CUH). https://www.cuh.nhs.uk/our-research/
  6. DeGrave AJ, Janizek JD, Lee SI. AI for radiographic COVID-19 detection selects shortcuts over signal. Nat Mach Intell. 2021 May;3(7):610–9. doi: 10.1038/s42256-021-00338-7
  7. Geirhos R, Jacobsen JH, Michaelis C, Zemel R, Brendel W, Bethge M, et al. Shortcut Learning in Deep Neural Networks. Nat Mach Intell. 2020;2:665–73. doi: 10.1038/s42256-020-00257-z
  8. Jabbour S, Fouhey D, Kazerooni E, Sjoding MW, Wiens J, Jabbour S, et al. Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts. Journal of Machine Learning Research. 2020 Sep;126:1–32. doi: 10.48550/arXiv.2009.10132
  9. Zhang R, Griner D, Garrett JW, Qi Z, Chen GH. Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets. Sci Rep. 2023 Aug;13(1):1–8. doi: 10.1038/s41598-023-39855-3
  10. Maguolo G, Nanni L. A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images. Information Fusion. 2021 Dec;76:1–7. doi: 10.1016/j.inffus.2021.04.008
GALLERY