PuSH - Publikationsserver des Helmholtz Zentrums München

Zeitschriften-Browsing

86 Datensätze gefunden.
Zum Exportieren der Ergebnisse bitte einloggen.
Alle Publikationen dieser Seite in den Korb legen
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)
2.
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.
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)
5.
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)
6.
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)
7.
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)
8.
Albarqouni, S. ; Khanal, B.* ; Rekik, I.* ; Rieke, N.* & Sheet, D.*: Preface FAIR 2021. Lect. Notes Comput. Sc. 12968 LNCS, vii-viii (2021)
9.
Albarqouni, S. et al.: Preface dart 2021. Lect. Notes Comput. Sc. 12968 LNCS, v-vi (2021)
10.
Baltatzis, V.* et al.: The effect of the loss on generalization: Empirical study on synthetic lung nodule data. In: (4th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020 and 1st International Workshop on Topological Data Analysis and Its Applications for Medical Data, TDA4MedicalData 2021 held in conjunction, 27 September 2021, Strasbourg). 2021. 56-64 (Lect. Notes Comput. Sc. ; 12929 LNCS)
11.
Baltatzis, V.* et al.: The pitfalls of sample selection: A case study on lung nodule classification. In: (4th International Workshop on Predictive Intelligence in Medicine, PRIME 2021, 1 October 2021, Virtual, Online). 2021. 201-211 (Lect. Notes Comput. Sc. ; 12928 LNCS)
12.
Bdair, T.* ; Navab, N.* & Albarqouni, S.: Peer learning for skin lesion classification. In:. 2021. 336–346 (Lect. Notes Comput. Sc.)
13.
Bdair, T. ; Navab, N.* & Albarqouni, S.: FedPerl: Semi-supervised peer learning for skin lesion classification. In: (24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, 27 September-01 October 2021, Virtual, Online). 2021. 336-346 (Lect. Notes Comput. Sc. ; 12903 LNCS)
14.
Bukas, C. et al.: Patient-specific virtual spine straightening andvertebra inpainting: An automatic framework forosteoplasty planning. In: MICCAI 2021: Medical Image Computing and Computer Assisted Intervention. 2021. 529–539 (Lect. Notes Comput. Sc.)
15.
Bukas, C. et al.: Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning. In: (24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, 27 September-01 October 2021, Virtual, Online). 2021. 529-539 (Lect. Notes Comput. Sc. ; 12904 LNCS)
16.
Dima, A.* et al.: Segmentation of peripancreatic arteries in multispectral computed tomography imaging. In: (12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, 27 September 2021, Virtual, Online). 2021. 596-605 (Lect. Notes Comput. Sc. ; 12966 LNCS)
17.
Han, X.* ; Zhai, Y.* ; Yu, Z.* ; Peng, T. & Zhang, X.Y.*: Detecting extremely small lesions in mouse brain MRI with point annotations via multi-task learning. In: (12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, 27 September 2021, Virtual, Online). 2021. 498-506 (Lect. Notes Comput. Sc. ; 12966 LNCS)
18.
Rekik, I.* ; Adeli, E.* ; Park, S.H.* & Schnabel, J.A.: Preface. Lect. Notes Comput. Sc. 12928 LNCS, v-vii (2021)
19.
Sadafi, A. et al.: Sickle cell disease severity prediction from percoll gradient images using graph convolutional networks. In: (3rd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, 27 September-01 October 2021, Virtual, Online). 2021. 216-225 (Lect. Notes Comput. Sc. ; 12968 LNCS)
20.
Shahzadi, I.* et al.: Do we need complex image features to personalize treatment of patients with locally advanced rectal cancer? In: (24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, 27 September-01 October 2021, Virtual, Online). 2021. 775-785 (Lect. Notes Comput. Sc. ; 12907 LNCS)