Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Petersen, Eike"'
Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical utility, safety, and fairness. This can affect known patient groups -
Externí odkaz:
http://arxiv.org/abs/2406.12142
Shortcut learning is when a model -- e.g. a cardiac disease classifier -- exploits correlations between the target label and a spurious shortcut feature, e.g. a pacemaker, to predict the target label based on the shortcut rather than real discriminat
Externí odkaz:
http://arxiv.org/abs/2312.14223
While many studies have assessed the fairness of AI algorithms in the medical field, the causes of differences in prediction performance are often unknown. This lack of knowledge about the causes of bias hampers the efficacy of bias mitigation, as ev
Externí odkaz:
http://arxiv.org/abs/2308.05129
Medical imaging models have been shown to encode information about patient demographics such as age, race, and sex in their latent representation, raising concerns about their potential for discrimination. Here, we ask whether requiring models not to
Externí odkaz:
http://arxiv.org/abs/2305.01397
Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set. However, in practice these annotations can differ systematically in the way they
Externí odkaz:
http://arxiv.org/abs/2303.15850
Recent work on algorithmic fairness has largely focused on the fairness of discrete decisions, or classifications. While such decisions are often based on risk score models, the fairness of the risk models themselves has received considerably less at
Externí odkaz:
http://arxiv.org/abs/2302.08851
Autor:
Petersen, Eike, Feragen, Aasa, Zemsch, Maria Luise da Costa, Henriksen, Anders, Christensen, Oskar Eiler Wiese, Ganz, Melanie
Convolutional neural networks have enabled significant improvements in medical image-based diagnosis. It is, however, increasingly clear that these models are susceptible to performance degradation when facing spurious correlations and dataset shift,
Externí odkaz:
http://arxiv.org/abs/2204.01737
Autor:
Petersen, Eike, Potdevin, Yannik, Mohammadi, Esfandiar, Zidowitz, Stephan, Breyer, Sabrina, Nowotka, Dirk, Henn, Sandra, Pechmann, Ludwig, Leucker, Martin, Rostalski, Philipp, Herzog, Christian
Publikováno v:
IEEE Access, Vol. 10, pp. 58375-58418, 2022
Machine learning is expected to fuel significant improvements in medical care. To ensure that fundamental principles such as beneficence, respect for human autonomy, prevention of harm, justice, privacy, and transparency are respected, medical machin
Externí odkaz:
http://arxiv.org/abs/2107.09546
Publikováno v:
In Patterns 14 July 2023 4(7)
Publikováno v:
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
The electromyogram (EMG) is an important tool for assessing the activity of a muscle and thus also a valuable measure for the diagnosis and control of respiratory support. In this article we propose convolutive blind source separation (BSS) as an eff
Externí odkaz:
http://arxiv.org/abs/1904.04083