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1.
An, U.* ; Cai, N. ; Dahl, A.* & Sankararaman, S.*: AutoComplete: Deep learning-based phenotype imputation for large-scale biomedical data. In: (26th International Conference on Research in Computational Molecular Biology, RECOMB 2022, 22-25 May 2022, San Diego, California, United States). 2022. 385-386 (Lect. Notes Comput. Sc. ; 13278 LNBI)
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Heinrich, M.* et al.: Preface. Lect. Notes Comput. Sc. 13386 LNCS, v-vi (2022)
3.
Horoi, S.* et al.: Exploring the geometry and topology of neural network loss landscapes. In: (20th International Symposium on Intelligent Data Analysis, IDA 2022, 20-22 April 2022, Rennes). 2022. 171-184 (Lect. Notes Comput. Sc. ; 13205 LNCS)
4.
Jian, B.* et al.: Weakly-supervised biomechanically-constrained CT/MRI registration of the spine. In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022). 2022. 227-236 (Lect. Notes Comput. Sc. ; 13436 LNCS)
5.
Kazeminia, S. et al.: Anomaly-aware multiple instance learning for rare anemia disorder classification. In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022). 2022. 341-350 (Lect. Notes Comput. Sc. ; 13438 LNCS)
6.
Koch, V. et al.: Noise transfer for unsupervised domain adaptation of retinal OCT images. In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022). 2022. 699-708 (Lect. Notes Comput. Sc. ; 13432 LNCS)
7.
Lang, D.M. ; Peeken, J.C. ; Combs, S.E. ; Wilkens, J.J.* & Bartzsch, S.: Deep learning based GTV delineation and progression free survival risk score prediction for head and neck cancer patients. In: (HECKTOR 2021: Head and Neck Tumor Segmentation and Outcome Prediction, 27 September 2021, Virtual, Online). 2022. 150-159 (Lect. Notes Comput. Sc. ; 13209 LNCS)
8.
Lang, D.M. ; Peeken, J.C. ; Combs, S.E. ; Wilkens, J.J.* & Bartzsch, S.: A video data based transfer learning approach for classification of MGMT status in brain tumor MR images. In:. 2022. 306-314 (Lect. Notes Comput. Sc. ; 12962 LNCS)
9.
Liu, Y.* et al.: DeStripe: A Self2Self spatio-spectral graph neural network with unfolded hessian for stripe artifact removal in light-sheet microscopy. In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022). 2022. 99-108 (Lect. Notes Comput. Sc. ; 13434 LNCS)
10.
Machado, I.* et al.: Quality-aware cine cardiac MRI reconstruction and analysis from undersampled K-space data. In: (Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge). 2022. 12-20 (Lect. Notes Comput. Sc. ; 13131 LNCS)
11.
Mächler, L.* et al.: FedCostWAvg: A new averaging for better federated learning. In:. 2022. 383-391 (Lect. Notes Comput. Sc. ; 12963 LNCS)
12.
Reisenbüchler, D. ; Wagner, S. ; Boxberg, M.* & Peng, T.: Local attention graph-based transformer for multi-target genetic alteration prediction. In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022). 2022. 377-386 (Lect. Notes Comput. Sc. ; 13432 LNCS)
13.
Salehi, R. et al.: Unsupervised cross-domain feature extraction for single blood cell image classification. In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022). 2022. 739-748 (Lect. Notes Comput. Sc. ; 13433 LNCS)
14.
Shetab Boushehri, S. ; Qasim, A.B. ; Waibel, D.J.E. ; Schmich, F.* & Marr, C.: Systematic comparison of incomplete-supervision approaches for biomedical image classification. In: (International Conference on Artificial Neural Networks). 2022. 355-365 (Lect. Notes Comput. Sc. ; 13529 LNCS)
15.
Starke, S.* ; Thalmeier, D. ; Steinbach, P.* & Piraud, M.: A hybrid radiomics approach to modeling progression-free survival in head and neck cancers. In: (HECKTOR 2021: Head and Neck Tumor Segmentation and Outcome Prediction, Virtual, Online). 2022. 266-277 (Lect. Notes Comput. Sc. ; 13209 LNCS)
16.
Tomczak, A.* ; Gupta, A.* ; Ilic, S.* ; Navab, N.* & Albarqouni, S.: What can we learn about a generated image corrupting its latent representation? In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022). 2022. 505-515 (Lect. Notes Comput. Sc. ; 13436 LNCS)
17.
Tran, M. ; Wagner, S. ; Boxberg, M.* & Peng, T.: S5CL: Unifying fully-supervised, self-supervised, and semi-supervised learning through hierarchical contrastive learning. In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022). 2022. 99-108 (Lect. Notes Comput. Sc. ; 13432 LNCS)
18.
Ugurlu, D.* et al.: The impact of domain shift on left and right ventricle segmentation in short axis cardiac MR images. In: (STACOM 2021: Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge, 27 September 2021, Strasbourg). 2022. 57-65 (Lect. Notes Comput. Sc. ; 13131 LNCS)
19.
Waibel, D.J.E. ; Atwell, S. ; Meier, M. ; Marr, C. & Rieck, B.: Capturing shape information with multi-scale topological loss terms for 3D reconstruction. In:. 2022. 150-159 (Lect. Notes Comput. Sc. ; 13434 LNCS)
20.
Albarqouni, S. ; Khanal, B.* ; Rekik, I.* ; Rieke, N.* & Sheet, D.*: Preface FAIR 2021. Lect. Notes Comput. Sc. 12968 LNCS, vii-viii (2021)