Mitigating Unknown Bias in Deep Learning-based Assessment of CT Images DeepTechnome
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Publisher
Springer Fachmedien Wiesbaden
Link
https://link.springer.com/content/pdf/10.1007/978-3-658-41657-7_38
Reference9 articles.
1. Mühlberg A, Katzmann A, Heinemann V, et al. The technome - a predictive internal calibration approach for quantitative imaging biomarker research. Sci Rep. 2020;10(1103).
2. Taubmann O, Berger M, Bögel M, et al. Computed tomography. Medical imaging systems. Ed. by Maier A, et al. Springer, 2018. Chap. 8:147–89.
3. Choe J, Lee SD, Do K, et al. Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses. Radiol. 2019;292 2:365–73.
4. Kim B, Kim H, Kim K, et al. Learning not to learn: training deep neural networks with biased data. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2019:9004–12.
5. Amini A, Soleimany A, Schwarting W, et al. Uncovering and mitigating algorithmic bias through learned latent structure. Proc Conf AAAI/ACM AI, Ethics, and Society. 2019:289– 95.
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