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161.
Rekik, I.* ; Adeli, E.* ; Park, S.H.* & Schnabel, J.A.: Preface. Lect. Notes Comput. Sc. 12928 LNCS, v-vii (2021)
162.
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)
163.
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)
164.
Shit, S.* ; Ezhov, I.* ; Paetzold, J.C. & Menze, B.*: A ν -Net: Automatic detection and segmentation of aneurysm. In: International workshop on Cerebral Aneurysm Detection. 2021. 51-57 (Lect. Notes Comput. Sc. ; 12643 LNCS)
165.
Varela-Salinas, G.* et al.: A binary classification model for toxicity prediction in drug design. In: (16th International Conference on Hybrid Artificial Intelligent Systems, HAIS 202, 22-24 September 2021, Bilbao). 2021. 149-157 (Lect. Notes Comput. Sc. ; 12886 LNAI)
166.
Wagner, S.J.* et al.: Structure-preserving multi-domain stain color augmentation using style-transfer with disentangled representations. In: (24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, 27 September-01 October 2021, Virtual, Online). 2021. 257-266 (Lect. Notes Comput. Sc. ; 12908 LNCS)
167.
Yu, Z.* ; Zhai, Y.* ; Han, X.* ; Peng, T. & Zhang, X.Y.*: MouseGAN: GAN-based multiple MRI modalities synthesis and segmentation for mouse brain structures. In: (MICCAI 2021: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 27 September-01 October 2021, Virtual, Online). 2021. 442-450 (Lect. Notes Comput. Sc. ; 12901 LNCS)
168.
Ansari, M. ; Fischer, D.S. & Theis, F.J.: Learning Tn5 sequence bias from ATAC-seq on naked chromatin. Lect. Notes Comput. Sc. 12396 LNCS, 105-114 (2020)
169.
Gerl, S.* et al.: A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images. Lect. Notes Comput. Sc. 12266 LNCS, 309-319 (2020)
170.
Peng, T. et al.: Background and illumination correction for time-lapse microscopy data with correlated foreground. In:. 2020. 174-183 (Lect. Notes Comput. Sc. ; 12265 LNCS)
171.
Sadafi, A. et al.: Attention based multiple instance learning for classification of blood cell disorders. In:. 2020. 246-256 (Lect. Notes Comput. Sc. ; 12265 LNCS)
172.
Senapati, J.* et al.: Bayesian neural networks for uncertainty estimation of imaging biomarkers. Lect. Notes Comput. Sc. 12436 LNCS, 270-280 (2020)
173.
Behzadi, S.* ; Müller, N.S. ; Plant, C.* & Böhm, C.*: Clustering of mixed-type data considering concept hierarchies. Lect. Notes Comput. Sc. 11439 LNAI, 555-573 (2019)
174.
Ghosh, D.* ; Tetko, I.V. ; Klebl, B.* ; Nussbaumer, P.* & Koch, U.*: Analysis and modelling of false positives in GPCR assays. Lect. Notes Comput. Sc. 11731 LNCS, 764-770 (2019)
175.
Karpov, P. ; Godin, G.* & Tetko, I.V.: A transformer model for retrosynthesis. Lect. Notes Comput. Sc. 11731 LNCS, 817-830 (2019)
176.
Mishra, M. et al.: Quantifying structural heterogeneity of healthy and cancerous mitochondria using a combined segmentation and classification USK-net. Lect. Notes Comput. Sc. 11731 LNCS, 289-298 (2019)
177.
Peng, T. ; Boxberg, M.* ; Weichert, W.* ; Navab, N.* & Marr, C.: Multi-task learning of a deep K-nearest neighbour network for histopathological image classification and retrieval. Lect. Notes Comput. Sc. 11764 LNCS, 676-684 (2019)
178.
Sadafi, A. et al.: Multiclass deep active learning for detecting red blood cell subtypes in brightfield microscopy. Lect. Notes Comput. Sc. 11764 LNCS, 685-693 (2019)
179.
Schneider, M. ; Wang, L. & Marr, C.: Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets. Lect. Notes Comput. Sc. 11728 LNCS, 673-686 (2019)
180.
Schulte-Sasse, R.* ; Budach, S.* ; Hnisz, D.* & Marsico, A.: Graph convolutional networks improve the prediction of cancer driver genes. Lect. Notes Comput. Sc. 11731 LNCS, 658-668 (2019)
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