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1.
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)
2.
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)
3.
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)
4.
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)
5.
Senapati, J.* et al.: Bayesian neural networks for uncertainty estimation of imaging biomarkers. Lect. Notes Comput. Sc. 12436 LNCS, 270-280 (2020)
6.
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)
7.
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)
8.
Karpov, P. ; Godin, G.* & Tetko, I.V.: A transformer model for retrosynthesis. Lect. Notes Comput. Sc. 11731 LNCS, 817-830 (2019)
9.
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)
10.
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)
11.
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)
12.
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)
13.
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)
14.
Tetko, I.V. ; Theis, F.J.* ; Karpov, P. & Kůrková, V.: Preface. Lect. Notes Comput. Sc. 11727 LNCS, v-vii (2019)
15.
Tetko, I.V. ; Theis, F.J. ; Karpov, P. & Kůrková, V.*: Preface. Lect. Notes Comput. Sc. 11728 LNCS, v-vii (2019)
16.
Tetko, I.V. ; Theis, F.J. ; Karpov, P. & Kůrková, V.*: Preface. Lect. Notes Comput. Sc. 11729 LNCS, v-vii (2019)
17.
Tetko, I.V. ; Karpov, P. ; Bruno, E.* ; Kimber, T.B.* & Godin, G.*: Augmentation is what you need! Lect. Notes Comput. Sc. 11731 LNCS, 831-835 (2019)
18.
Tetko, I.V. ; Theis, F.J. ; Karpov, P. & Kůrková, V.*: Preface. Lect. Notes Comput. Sc. 11731 LNCS, v-vii (2019)
19.
Waibel, D.J.E. ; Tiemann, U.* ; Lupperger, V. ; Semb, H.* & Marr, C.: In-silico staining from bright-field and fluorescent images using deep learning. In:. 2019. 184-186 (Lect. Notes Comput. Sc. ; 11729 LNCS)
20.
Gutiérrez-Becker, B.* et al.: Deep shape analysis on abdominal organs for diabetes prediction. Lect. Notes Comput. Sc. 11167 LNCS, 223-231 (2018)