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2022

E. Fita, Damrich, S., and Hamprecht, F. A., “The Algebraic Path Problem for Graph Metrics”, 39th International Conference on Machine Learning, PMLR. Proceedings , vol. 162. pp. 19178-19204, 2022.

S. Damrich, “Discovering Structure without Labels”, Heidelberg University. 2022.

Q. Garrido, Damrich, S., Jäger, A., Cerletti, D., Claassen, M., Najman, L., and Hamprecht, F. A., “Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder”, Bioinformatics, vol. 38 (Suppl 1), pp. i316-i324, 2022.

2021

D. Sitenko, Boll, B., and Schnörr, C., “Assignment Flow For Order-Constrained OCT Segmentation”, Int J Computer Vision, vol. 129, 2021.

D. Gonzalez-Alvarado, Zeilmann, A., and Schnörr, C., “Assignment Flows and Nonlocal PDEs on Graphs”, GCPR, in press. 2021.

D. Sitenko, Boll, B., and Schnörr, C., “Assignment Flows and Nonlocal PDEs on Graphs”, GCPR, in press. 2021.

M. Haußmann, “Bayesian Neural Networks for Probabilistic Machine Learning”. Heidelberg University, 2021.

A. Ruiz, “Deep k-segments: a generalization of k-means”, Heidelberg University, 2021.

A. Bailoni, “Deep Learning for Graph-Based Image Instance Segmentation”. Heidelberg University, 2021.

A. Vijayan, Tofanelli, R., Strauss, S., Cerrone, L., Wolny, A., Strohmeier, J., Kreshuk, A., Hamprecht, F. A., Smith, R. S., and Schneitz, K., “A Digital 3D Reference Atlas Reveals Cellular Growth Patterns Shaping the Arabidopsis Ovule”, eLife, 2021.

E. Fita, Damrich, S., and Hamprecht, F. A., “Directed Probabilistic Watershed”, NeurIPS. Proceedings, vol. 34. 2021.

Technical Report (957.78 KB)

M. Kandemir, Agkül, A., Haußmann, M., and Ünal, G., “Evidential Turing Processes”. arXiv preprint, 2021.

E. Jenner, Fita, E., and Hamprecht, F. A., “Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice”, ICCV. Proceedings. pp. 4602-4611, 2021.

Technical Report (1.1 MB)

L. M. Schütz, Louveaux, M., Vilches-Barro, A., Bouziri, S., Cerrone, L., Wolny, A., Kreshuk, A., Hamprecht, F. A., and Maizel, A., “Integration of Cell Growth and Asymmetric Division during Lateral Root Initiation in Arabidopsis thaliana”, Plant and Cell Physiology, vol. 62, no. 8, pp. 1269-1279, 2021.

A. Andersson, Diego, F., Hamprecht, F. A., and Wählby, C., “ISTDECO: In Situ Transcriptomics Decoding by Deconvolution”, bioRxiv, 2021.

M. Haußmann, Gerwinn, S., Look, A., Rakitsch, B., and Kandemir, M., “Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes”, International Conference on Artificial Intelligence and Statistics , vol. PMLR 130. pp. 478-486, 2021.

C. Pape, 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., “Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera”, BioEssays, vol. 43, no. 3, 2021.

F. C. Walter, Damrich, S., and Hamprecht, F. A., “MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons”, ISBI. pp. 295-298, 2021.

Technical Report (1.83 MB)

C. Pape, “Scalable Instance Segmentation for Microscopy”. Heidelberg University, 2021.

H. Arlt, 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., “Seipin forms a flexible cage at lipid droplet formation sites”. bioRxiv, 2021.

S. Damrich and Hamprecht, F. H., “UMAP does not reproduce high-dimensional similarities due to negative sampling”. arXiv preprint, 2021.

S. Damrich and Hamprecht, F. A., “On UMAP's True Loss Function”, NeurIPS. Proceedings, vol. 34. 2021.

Technical Report (1.87 MB)

M. Bellagente, Haußmann, M., Luchmann, M., and Plehn, T., “Understanding Event-Generation Networks via Uncertainties”. arXiv preprint, 2021.

2020

A. Wolny, 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., “Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution”, eLife, vol. 9, 2020.

M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Evidential Deep Learning with PAC Regularization ”, 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.

E. Kirschbaum, Bailoni, A., and Hamprecht, F. A., “DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging”, MICCAI. Proceedings. pp. 151-162, 2020.

S. Wolf, Hamprecht, F. A., and Funke, J., “Inpainting Networks Learn to Separate Cells in Microscopy Images”, BMCV. 2020.

Technical Report (357.23 KB)

S. Wolf, Hamprecht, F. A., and Funke, J., “Instance Separation Emerges from Inpainting”, arXiv preprint arXiv:2003.00891, 2020.

S. Wolf, “Machine Learning for Instance Segmentation”. Heidelberg University, 2020.

A. Bailoni, Pape, C., Wolf, S., Kreshuk, A., and Hamprecht, F. A., “Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks”, GCPR, vol. 12544. Springer, pp. 331-344, 2020.

S. Wolf, Li, Y., Pape, C., Bailoni, A., Kreshuk, A., and Hamprecht, F. A., “The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation”, ECCV. Proceedings. pp. 208-224, 2020.

2019

A. Imle, 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., “Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures”, Nature Communications, vol. 13;10(1), 2019.

F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, Comp. Statistics & Data Analysis, vol. 140, pp. 41-58, 2019.

S. Peter, “Machine learning under test-time budget constraints”. Heidelberg University, 2019.

A. Ravindran, “Novel Deep Learning-based Instance Segmentation Using Mutex Watershed for Microscopy Cell Images”, Heidelberg University, 2019.

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