Congress:
ECR25
Poster Number:
C-27255
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
M. Roschewitz, R. Mehta, C. Jones, B. Glocker; London/UK
Disclosures:
Mélanie Roschewitz:
Research/Grant Support: Google
Raghav Mehta:
Nothing to disclose
Charles Jones:
Research/Grant Support: Microsoft Research
Ben Glocker:
Employee: Kheiron Medical Technologies Employee: Heartflow
Keywords:
Artificial Intelligence, Computer applications, Conventional radiography, Mammography, Computer Applications-Detection, diagnosis, Quality assurance
- Finlayson Samuel G., Subbaswamy Adarsh, Singh Karandeep, Bowers John, Kupke Annabel, Zittrain Jonathan, Kohane Isaac S., and Saria Suchi, “The Clinician and Dataset Shift in Artificial Intelligence,” NEJM, vol. 385, pp. 283–286, July 2021.
- Seyyed-Kalantari, H. Zhang, M. B. A. McDermott, I. Y. Chen, and M. Ghassemi, “Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations,” Nature Medicine, vol. 27, pp. 2176–2182, Dec. 2021.
- M. Koch, C. F. Baumgartner, and P. Berens, “Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study,” npj Digital Medicine, vol. 7, pp. 1–11, May 2024.
- Rabanser, S. Gunnemann, and Z. Lipton, “Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift,” in Advances in Neural Information Processing Systems, vol. 32, 2019.
- C. Castro, I. Walker, and B. Glocker, “Causality matters in medical imaging,” Nature Communications, vol. 11, p. 3673, July 2020.
- Godau, P. Kalinowski, E. Christodoulou, A. Reinke, M. Tizabi, L. Ferrer, P. F. J¨ ager, and L. Maier-Hein, “Deployment of Image Analysis Algorithms Under Prevalence Shifts,”
- Garrucho, K. Kushibar, S. Jouide, O. Diaz, L. Igual, and K. Lekadir, “Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study,” Artificial Intelligence in Medicine, vol. 132, p. 102386, Oct. 2022.
- Roschewitz, G. Khara, J. Yearsley, N. Sharma, J. J. James, E. Ambrozay, A. Heroux, P. Kecskemethy, T. Rijken, and B. Glocker, “Automatic correction of performance drift under acquisition shift in medical image classification,” Nature Communications, vol. 14, p. 6608, Oct. 2023.
- Bustos, A. Pertusa, J.-M. Salinas, and M. de la Iglesia-Vaya, “PadChest: A large chest x-ray image dataset with multi-label annotated reports,” Medical Image Analysis, vol. 66, p. 101797, Dec. 2020.
- Shih, C. C. Wu, S. S. Halabi, M. D. Kohli, L. M. Prevedello, T. S. Cook, A. Sharma, J. K. Amorosa, V. Arteaga, M. Galperin-Aizenberg, R. R. Gill, M. C. Godoy, S. Hobbs, J. Jeudy, A. Laroia, P. N. Shah, D. Vummidi, K. Yaddanapudi, and A. Stein, “Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia,” Radiology: Artificial Intelligence, vol. 1, p. e180041, Jan. 2019.
- J. Jeong, B. L. Vey, A. Bhimireddy, T. Kim, T. Santos, R. Correa, R. Dutt, M. Mosunjac, G. Oprea-Ilies, G. Smith, M. Woo, C. R. McAdams, M. S. Newell, I. Banerjee, J. Gichoya, and H. Trivedi, “The Emory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images,” Radiology: Artificial Intelligence, vol. 5, p. e220047, Jan. 2023.
- Dugas, J. Jared, and W. Cukierski, “Diabetic Retinopathy Detection Kaggle Challenge,” 2015.
- Karthik and D. Sohier, “APTOS 2019 Blindness Detection Kaggle Challenge,” 2019.
- Decenciere, X. Zhang, G. Cazuguel, B. Lay, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J.-C. Klein, “Feedback on a publicly distributed image database: the messidor database,” Image Analysis and Stereology, vol. 33, pp. 231–234, Aug. 2014.