All Publications


Fita, E, Damrich, S and Hamprecht, F A (2022). The Algebraic Path Problem for Graph Metrics. 39th International Conference on Machine Learning, PMLR. Proceedings . 162 19178-19204
Garrido, Q, Damrich, S, Jäger, A, Cerletti, D, Claassen, M, Najman, L and Hamprecht, F A (2022). Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder. Bioinformatics. arXiv preprint. 38 (Suppl 1) i316-i324
Damrich, S (2022). Discovering Structure without Labels. Heidelberg University


Arlt, H, Sui, X, Folger, B, Adams, C, Chen, X, Remme, R, Hamprecht, F A, DiMaio, F, Liao, M, Goodman, J M, Farese, R V and Walther, T C (2021). Seipin forms a flexible cage at lipid droplet formation sites. bioRxiv
Jenner, E, Fita, E and Hamprecht, F A (2021). Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice. ICCV. Proceedings. 4602-4611PDF icon Technical Report (1.1 MB)
Schütz, L M, Louveaux, M, Vilches-Barro, A, Bouziri, S, Cerrone, L, Wolny, A, Kreshuk, A, Hamprecht, F A and Maizel, A (2021). Integration of Cell Growth and Asymmetric Division during Lateral Root Initiation in Arabidopsis thaliana. Plant and Cell Physiology. 62 1269-1279
Damrich, S and Hamprecht, F A (2021). On UMAP's True Loss Function. NeurIPS. Proceedings. 34PDF icon Technical Report (1.87 MB)
Fita, E, Damrich, S and Hamprecht, F A (2021). Directed Probabilistic Watershed. NeurIPS. Proceedings. 34PDF icon Technical Report (957.78 KB)
Ruiz, A (2021). Deep K-Segments: A Generalization Of K-Means. Heidelberg University
Haußmann, (2021). Bayesian Neural Networks for Probabilistic Machine Learning. Heidelberg University
Bailoni, A (2021). Deep Learning for Graph-Based Image Instance Segmentation. Heidelberg University
Pape, C (2021). Scalable Instance Segmentation for Microscopy. Heidelberg University
Bellagente, M, Haußmann, M, Luchmann, M and Plehn, T (2021). Understanding Event-Generation Networks via Uncertainties. arXiv preprint.
Kandemir, M, Agkül, A, Haußmann, M and Ünal, G (2021). Evidential Turing Processes. arXiv preprint.
Haußmann, M, Gerwinn, S, Look, A, Rakitsch, B and Kandemir, M (2021). Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. International Conference on Artificial Intelligence and Statistics . PMLR 130 478-486
Andersson, A, Diego, F, Hamprecht, F A and Wählby, C (2021). Istdeco: In Situ Transcriptomics Decoding By Deconvolution. bioRxiv
Damrich, S and Hamprecht, F H (2021). UMAP does not reproduce high-dimensional similarities due to negative sampling. arXiv preprint
Walter, F C, Damrich, S and Hamprecht, F A (2021). MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons. ISBI. 295-298PDF icon Technical Report (1.83 MB)
Vijayan, A, Tofanelli, R, Strauss, S, Cerrone, L, Wolny, A, Strohmeier, J, Kreshuk, A, Hamprecht, F A, Smith, R S and Schneitz, K (2021). A Digital 3D Reference Atlas Reveals Cellular Growth Patterns Shaping the Arabidopsis Ovule. eLife
Pape, C, Remme, R, Wolny, A, Olberg, S, Wolf, S, Cerrone, L, Cortese, M, Klaus, S, Lucic, B, Ullrich, S, Anders-Össwein, M, Wolf, S, Cerikan, B, Neufeldt, C J, Ganter, M, Schnitzler, P, Merle, U, Lusic, M, Boulant, S, Stanifer, M, Bartenschlager, R, Hamprecht, F A, Kreshuk, A, Tischer, C, Kräusslich, H - G, Müller, B and Laketa, V (2021). Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera. BioEssays. 43


Wolf, S, Hamprecht, F A and Funke, J (2020). Instance Separation Emerges from Inpainting. arXiv preprint arXiv:2003.00891
Hehn, T M, Kooij, J F P and Hamprecht, F A (2020). End-to-End Learning of Decision Trees and Forests. International Journal of Computer Vision. 128 997-1011
Bollweg, S, Haußmann, M, Kasieczka, G, Luchmann, M, Plehn, T and Thompson, J (2020). Deep-Learning Jets with Uncertainties and More. SciPost Phys. 8. icon Technical Report (1.65 MB)
Wolf, S, Bailoni, A, Pape, C, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2020). The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43 3724-3738PDF icon Technical Report (2.58 MB)
Wolf, S (2020). Machine Learning for Instance Segmentation. Heidelberg University
Wolf, S, Li, Y, Pape, C, Bailoni, A, Kreshuk, A and Hamprecht, F A (2020). The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation. ECCV. Proceedings. 208-224
Wolf, S, Hamprecht, F A and Funke, J (2020). Inpainting Networks Learn to Separate Cells in Microscopy Images. BMCVPDF icon Technical Report (357.23 KB)
Wolny, A, Cerrone, L, Vijayan, A, Tofanelli, R, Vilches-Barro, A, Louveaux, M, Wenzel, C, Strauss, S, Wilson-Sanchez, D, Lymbouridou, R, Steigleder, S S, Pape, C, Bailoni, A, Duran-Nebreda, S, Bassel, G W, Lohmann, J U, Tsiantis, M, Hamprecht, F A, Schneitz, K, Maizel, A and Kreshuk, A (2020). Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution. eLife. 9
Kirschbaum, E, Bailoni, A and Hamprecht, F A (2020). DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging. MICCAI. Proceedings. 151-162
Haußmann, M, Gerwinn, S and Kandemir, M (2020). Bayesian Evidential Deep Learning with PAC Regularization . 3rd Symposium on Advances in Approximate Bayesian Inference
Bailoni, A, Pape, C, Wolf, S, Kreshuk, A and Hamprecht, F A (2020). Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks. GCPR. Springer. 12544 331-344


Bengio, Y, Deleu, T, Rahaman, N, Ke, R, Lachapelle, S, Bilaniuk, O, Goyal, A and Pal, C (2019). A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. arXiv preprint arXiv:1901.10912PDF icon Technical Report (871.59 KB)
Kiefer, L, Storath, M and Weinmann, A (2019). An efficient algorithm for the piecewise affine-linear Mumford-Shah model based on a Taylor jet splitting. IEEE Transactions on Image Processing. 29PDF icon Technical Report (2.04 MB)
Berg, S, Kutra, D, Kroeger, T, Straehle, C N, Kausler, B X, Haubold, C, Schiegg, M, Ales, J, Beier, T, Rudy, M, Eren, K, Cervantes, J I, Xu, B, Beuttenmüller, F, Wolny, A, Zhang, C, Köthe, U, Hamprecht, F A and Kreshuk, A (2019). ilastik: interactive machine learning for (bio)image analysis. Nature Methods. 16 1226-1232
Haußmann, M, Gerwinn, S and Kandemir, M (2019). Bayesian Prior Networks with PAC Training. arXiv preprint arXiv:1906.00816
Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Deep Active Learning with Adaptive Acquisition. IJCAI. Proceedings. 2470-2476PDF icon Technical Report (137.6 KB)
Cerrone, L, Zeilmann, A and Hamprecht, F A (2019). End-to-End Learned Random Walker for Seeded Image Segmentation. CVPR. Proceedings. 12559-12568
Ravindran, A (2019). Novel Deep Learning-Based Instance Segmentation Using Mutex Watershed For Microscopy Cell Images. Heidelberg University
Li, J (2019). Robust Single Object Tracking Via Fully Convolutional Siamese Networks. Heidelberg University
Bendinger, A L, Debus, C, Glowa, C, Karger, C P, Peter, J and Storath, M (2019). Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models, in press. Physics in Medicine and Biology. 64