Damrich, S (2022). Discovering Structure without Labels. Heidelberg University
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


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
Haußmann, (2021). Bayesian Neural Networks for Probabilistic Machine Learning. Heidelberg University
Ruiz, A (2021). Deep K-Segments: A Generalization Of K-Means. Heidelberg University
Bailoni, A (2021). Deep Learning for Graph-Based Image Instance Segmentation. Heidelberg University
Fita, E, Damrich, S and Hamprecht, F A (2021). Directed Probabilistic Watershed. NeurIPS. Proceedings. 34PDF icon Technical Report (957.78 KB)
Kandemir, M, Agkül, A, Haußmann, M and Ünal, G (2021). Evidential Turing Processes. arXiv preprint.
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
Andersson, A, Diego, F, Hamprecht, F A and Wählby, C (2021). Istdeco: In Situ Transcriptomics Decoding By Deconvolution. bioRxiv
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
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
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)
Damrich, S and Hamprecht, F A (2021). On UMAP's True Loss Function. NeurIPS. Proceedings. 34PDF icon Technical Report (1.87 MB)
Pape, C (2021). Scalable Instance Segmentation for Microscopy. 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
Damrich, S and Hamprecht, F H (2021). UMAP does not reproduce high-dimensional similarities due to negative sampling. arXiv preprint
Bellagente, M, Haußmann, M, Luchmann, M and Plehn, T (2021). Understanding Event-Generation Networks via Uncertainties. arXiv preprint.


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
Haußmann, M, Gerwinn, S and Kandemir, M (2020). Bayesian Evidential Deep Learning with PAC Regularization . 3rd Symposium on Advances in Approximate Bayesian Inference
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)
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
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
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)
Wolf, S, Hamprecht, F A and Funke, J (2020). Instance Separation Emerges from Inpainting. arXiv preprint arXiv:2003.00891
Wolf, S (2020). Machine Learning for Instance Segmentation. Heidelberg University
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
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, 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


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)
Haußmann, M, Gerwinn, S and Kandemir, M (2019). Bayesian Prior Networks with PAC Training. arXiv preprint arXiv:1906.00816
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
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
Imle, A, Kumberger, P, Schnellbächer, N D, Fehr, J, Carillo-Bustamente, P, Ales, J, Schmidt, P, Ritter, C, Godinez, W J, Müller, B, Rohr, K, Hamprecht, F A, Schwarz, U S, Graw, F and Fackler, O T (2019). Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures. Nature Communications. 13;10(1)
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
Remme, R (2019). Instance Segmentation Via Associative Pixel Embeddings. Heidelberg University