Author:
Valsson Steinar,Arandjelović Ognjen
Publisher
Springer International Publishing
Reference22 articles.
1. Baltruschat, I. M., Nickisch, H., Grass, M., Knopp, T., & Saalbach, A. (2019). Comparison of deep learning approaches for multi-label chest X-ray classification. Scientific Reports, 9(1), 1–10.
2. Boone, D., Mallett, S., Zhu, S., Yao, G. L., Bell, N., Ghanouni, A., von Wagner, C., Taylor, S. A., Altman, D. G., Lilford, R., & Halligan, S. (2013). Patients’ & healthcare professionals’ values regarding true- & false-positive diagnosis when colorectal cancer screening by CT colonography: Discrete choice experiment. PLoS ONE, 8(12), e80767.
3. Center for Devices and Radiological Health, Food And Drug Administration: Software as a Medical Device (SAMD): Clinical Evaluation. Technical Report, FDA, Center for Devices and Radiological Health (2018). https://www.fda.gov/media/100714/download.
4. Cheung, T., Harianto, H., Spanger, M., Young, A., & Wadhwa, V. (2018). Low accuracy and confidence in chest radiograph interpretation amongst junior doctors and medical students. Internal Medicine Journal, 48(7), 864–868.
5. Cooper, J., Arandjelović, O., & Harrison, D. (2021). Believe the hipe: Hierarchical perturbation for fast and robust explanation of black box models. arXiv:2103.05108.