Hehn, T (2017). A Probabilistic Approach To Learn Complex Differentiable Split Functions In Decision Trees Using Gradient Ascent. Heidelberg University |
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Active machine learning for training an event classification. Patent, Patent Number WO2017032775 A1 |
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 1141-1152 Technical Report (4.24 MB) |
Brosowsky, M (2017). Cluster Resolving For Animal Tracking: Multi Hypotheses Tracking With Part Based Model For Object Hypotheses Generation And Pose Estimation. University of Heidelberg |
Krause, G (2017). Correlation Of Performance And Entropy In Active Learning With Convolutional Neural Networks. Heidelberg University |
Peter, S, Diego, F, Hamprecht, F A and Nadler, B (2017). Cost-efficient Gradient Boosting. NIPS, poster |
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 |
Uhlmann, V, Haubold, C, Hamprecht, F A and Unser, M (2017). Diverse Shortest Paths for Bioimage Analysis. Bioinformatics. 1-3 |
Storath, M, Brandt, C, Hofmann, M, Knopp, T, Salamon, J, Weber, A and Weinmann, A (2017). Edge preserving and noise reducing reconstruction for magnetic particle imaging. IEEE Transactions on Medical Imaging. 36 74 - 85 Technical Report (1.43 MB) |
Storath, M, Rickert, D, Unser, M and Weinmann, A (2017). Fast segmentation from blurred data in 3D fluorescence microscopy. IEEE Transactions on Image Processing. 26 |
Hennies, J (2017). Improvement And Validation Of Neural Em Volume Image Segmentation By High-Level Information. University of Heidelberg |
Haller, A (2017). Interactive Watershed Based Segmentation For Biological Images. University of Heidelberg |
Storath, M, Weinmann, A and Unser, M (2017). Jump-penalized least absolute values estimation of scalar or circle-valued signals. Information and Inference. 6 225–245 Technical Report (3.4 MB) |
Schott, L (2017). Learned Watershed Algorithm: End-To-End Learning Of Seeded Segmentation. Heidelberg University |
Wolf, S, Schott, L, Köthe, U and Hamprecht, F A (2017). Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV. 2030-2038 Technical Report (3.76 MB) |
Weiler, M (2017). Learning Steerable Filters For Rotation Equivariant Convolutional Neural Networks. Heidelberg University |
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Maschinelles Lernen. Patent, Patent Number WO2017032775A1 Technical Report (317.04 KB) |
Balluff, B, Hanselmann, M and Heeren, R M A (2017). Mass spectrometry imaging for the investigation of intratumor heterogeneity. Advances in Cancer Research. Elsevier. 134 201-230 |
Beier, T, Pape, C, Rahaman, N, Prange, T, Berg, S, Bock, D, Cardona, A, Knott, G W, Plaza, S M, Scheffer, L K, Köthe, U, Kreshuk, A and Hamprecht, F A (2017). Multicut brings automated neurite segmentation closer to human performance. Nature Methods. 14 101-102. http://rdcu.be/oVDQ |
Krasowki, N, Beier, T, Knott, G W, Köthe, U, Hamprecht, F A and Kreshuk, A (2017). Neuron Segmentation with High-Level Biological Priors. IEEE Transactions on Medical Imaging. 37 |
Haubold, C (2017). Scalable Inference for Multi-Target Tracking on Proliferating Cells. University of Heidelberg |
Neigel, P (2017). Self-Similarity Based Detection Of Temporal Motifs In Multivariate Signals. Heidelberg University |
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 |
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 |
Haußmann, M, Hamprecht, F A and Kandemir, M (2017). Variational Bayesian Multiple Instance Learning with Gaussian Processes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6570-6579 Technical Report (1.29 MB) |