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1.
Bak, M.* et al.: Federated learning is not a cure-all for data ethics. Nat. Mach. Intell. 6, 370–372 (2024)
2.
Ziller, A.* et al.: Reconciling privacy and accuracy in AI for medical imaging. Nat. Mach. Intell., DOI: 10.1038/s42256-024-00858-y (2024)
3.
Debus, C.* ; Piraud, M. ; Streit, A.* ; Theis, F.J. & Götz, M.*: Reporting electricity consumption is essential for sustainable AI. Nat. Mach. Intell. 5, 1176-1178 (2023)
4.
Dehner, C. ; Zahnd, G. ; Ntziachristos, V. & Jüstel, D.: A deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound. Nat. Mach. Intell. 5, 1130–1141 (2023)
5.
Rädsch, T.* et al.: Labelling instructions matter in biomedical image analysis. Nat. Mach. Intell. 5, 273–283 (2023)
6.
Bercea, C.-I. ; Wiestler, B.* ; Rueckert, D.* & Albarqouni, S.: Federated disentangled representation learning for unsupervised brain anomaly detection. Nat. Mach. Intell. 4, 685-695 (2022)
7.
Schulte-Sasse, R.* ; Budach, S.* ; Hnisz, D.* & Marsico, A.: Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. Nat. Mach. Intell. 3, 513–526 (2021)
8.
Holmberg, O. et al.: Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy. Nat. Mach. Intell. 2, 719-726 (2020)
9.
Matek, C. ; Schwarz, S.* ; Spiekermann, K.* & Marr, C.: Human-level recognition of blast cells in acute myeloid leukemia with convolutional neural networks. Nat. Mach. Intell. 1, 538-544 (2019)
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