All Publications


Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Maschinelles Lernen. Patent, Patent Number WO2017032775A1PDF icon Technical Report (317.04 KB)
Haubold, C, Uhlmann, V, Unser, M and Hamprecht, F A (2017). Diverse M-best Solutions by Dynamic Programming. GCPR. Proceedings. Springer. LNCS 10496 255-267
Hennies, J (2017). Improvement And Validation Of Neural Em Volume Image Segmentation By High-Level Information. University of Heidelberg
Haubold, C (2017). Scalable Inference for Multi-Target Tracking on Proliferating Cells. University of Heidelberg
Schott, L (2017). Learned Watershed Algorithm: End-To-End Learning Of Seeded Segmentation. Heidelberg University
Peter, S, Diego, F, Hamprecht, F A and Nadler, B (2017). Cost-efficient Gradient Boosting. NIPS, poster
Peter, S, Kirschbaum, E, Both, M, Campbell, L A, Harvey, B K, Heins, C, Durstewitz, D, Diego, F and Hamprecht, F A (2017). Sparse convolutional coding for neuronal assembly detection. NIPS, poster
Uhlmann, V, Haubold, C, Hamprecht, F A and Unser, M (2017). Diverse Shortest Paths for Bioimage Analysis. Bioinformatics. 1-3
Ulman, V, Maška, M, Magnusson, K E G, Ronneberger, O, Haubold, C, Harder, N, Matula, P, Matula, P, Svoboda, D, Radojevic, M, Smal, I, Rohr, K, Jaldén, J, Blau, H M, Dzyubachyk, O, Lelieveldt, B, Xiao, P, Li, Y, Cho, S - Y, Dufour, A, Olivo-Marin, J C, Reyes-Aldasoro, C C, Solis-Lemus, J A, Bensch, R, Brox, T, Stegmaier, J, Mikut, R, Wolf, S, Hamprecht, F A, Esteves, T, Quelhas, P, Demirel, Ö, Malström, L, Jug, F, Tomančák, P, Meijering, E, Muñoz-Barrutia, A, Kozubek, M and Ortiz-de-Solorzano, C (2017). An Objective Comparison of Cell Tracking Algorithms. Nature Methods. 14 1141-1152PDF icon Technical Report (4.24 MB)
Wolf, S, Schott, L, Köthe, U and Hamprecht, F A (2017). Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV. 2030-2038PDF icon Technical Report (3.76 MB)
Pape, C, Beier, T, Li, P, Jain, V, Brock, D D and Kreshuk, A (2017). Solving Large Multicut Problems for Connectomics via Domain Decomposition. Bioimage Computing Workshop. ICCV. 1-10
Neigel, P (2017). Self-Similarity Based Detection Of Temporal Motifs In Multivariate Signals. Heidelberg University
Weiler, M (2017). Learning Steerable Filters For Rotation Equivariant Convolutional Neural Networks. Heidelberg University
Hehn, T (2017). A Probabilistic Approach To Learn Complex Differentiable Split Functions In Decision Trees Using Gradient Ascent. Heidelberg University
Krause, G (2017). Correlation Of Performance And Entropy In Active Learning With Convolutional Neural Networks. Heidelberg University


Haubold, C, Ales, J, Wolf, S and Hamprecht, F A (2016). A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets. ECCV. Proceedings. Springer. LNCS 9911 566-582PDF icon Technical Report (1.18 MB)
Kleesiek, J, Urban, G, Hubert, A, Schwarz, D, Maier-Hein, K, Bendszus, M and Biller, A (2016). Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.. NeuroImage. 129 460-469PDF icon Technical Report (1.14 MB)
Biller, A, Badde, S, Nagel, A, Neumann, J O, Wick, W, Hertenstein, A, Bendszus, M, Sahm, F, Benkhedah, N and Kleesiek, J (2016). Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression. American Journal of Neuroradiology. 37 66-73
von Borstel, M (2016). Learning To Count From Weak Supervision. University of Heidelberg
Prange, T (2016). Automatic Segmentation Of Neurons In Electron Microscopy Data With Membrane Defects. University of Heidelberg
Kleesiek, J, Petersen, J, Döring, M, Maier-Hein, K, Köthe, U, Wick, W, Hamprecht, F A, Bendszus, M and Biller, A (2016). Virtual Raters for Reproducible and Objective Assessments in Radiology. Nature Scientific Reports. 6PDF icon Technical Report (2.81 MB)
Haubold, C, Schiegg, M, Kreshuk, A, Berg, S, Köthe, U and Hamprecht, F A (2016). Segmenting and Tracking Multiple Dividing Targets Using ilastik. Focus on Bio-Image Informatics. Springer. 219 199-229PDF icon Technical Report (4.46 MB)
Stefanoiu, A, Weinmann, A, Storath, M, Navab, N and Baust, M (2016). Joint Segmentation and Shape Regularization with a Generalized Forward Backward Algorithm. IEEE Transactions on Image Processing. 25 3384 - 3394PDF icon Technical Report (3.55 MB)
Diego, F and Hamprecht, F A (2016). Structured Regression Gradient Boosting. CVPR. Proceedings. 1459-1467PDF icon Technical Report (3.97 MB)
Kiem, A (2016). Structured Learning On Calcium Imaging Data. University of Heidelberg
Pape, C (2016). Automatic Segmentation Of Neurites From Anisotropic Em-Imaging. University of Heidelberg
Krasowski, N (2016). Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors. University of Heidelberg
Beier, T, Andres, B, Köthe, U and Hamprecht, F A (2016). An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem. ECCV. Proceedings. Springer. LNCS 9906 715-730PDF icon Technical Report (4.89 MB)
Kandemir, M, Haußmann, M, Diego, F, Rajamani, K, van der Laak, J and Hamprecht, F A (2016). Variational weakly-supervised Gaussian processes. BMVC. ProceedingsPDF icon Technical Report (3.28 MB)
von Borstel, M, Kandemir, M, Schmidt, P, Rao, M, Rajamani, K and Hamprecht, F A (2016). Gaussian process density counting from weak supervision. ECCV. Proceedings. Springer. LNCS 9905 365-380 PDF icon Technical Report (1.71 MB)
Schiegg, M, Diego, F and Hamprecht, F A (2016). Learning Diverse Models: The Coulomb Structured Support Vector Machine. ECCV. Proceedings. Springer. LNCS 9907 585-599PDF icon Technical Report (2.54 MB)
Meijering, E, Carpenter, A E, Peng, H, Hamprecht, F A and Olivo-Marin, J (2016). Imagining the future of bioimage analysis. Nature Biotechnology. 34 1250-1255PDF icon Technical Report (924.57 KB)
Haußmann, (2016). Weakly Supervised Detection With Gaussian Processes. University of Heidelberg
Schmidt, P (2016). Deep Learning For Bioimage Analysis. University of Heidelberg
Rathore, D (2016). Semantic Segmentation Using Deep Learning. University of Heidelberg
Baust, M, Weinmann, A, Wieczorek, M, Lasser, T, Storath, M and Navab, N (2016). Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging based on a Riemannian Manifold Approach. IEEE Transactions on Medical Imaging. 35 1972–1989PDF icon Technical Report (8.65 MB)
Wolf, S (2016). Cell Tracking With Graphical Model Using Higher Order Features On Track Segments. University of Heidelberg
Balles, L (2016). Deep Learning For Diabetic Retinopathy Diagnostics. University of Heidelberg


Krasowski, N, Beier, T, Knott, G W, Köthe, U, Hamprecht, F A and Kreshuk, A (2015). Improving 3D EM Data Segmentation by Joint Optimization over Boundary Evidence and Biological Priors. 12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015. 536-539PDF icon Technical Report (2.25 MB)
Schiegg, M (2015). Multi-Target Tracking with Probabilistic Graphical Models. University of Heidelberg