Publications

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Hehn, T and Hamprecht, F A (2018). End-to-end Learning of Deterministic Decision Trees. German Conference on Pattern Recognition. Proceedings. Springer. LNCS 11269 612-627PDF icon Technical Report (1.4 MB)
Hehn, T M, Kooij, J F P and Hamprecht, F A (2019). End-to-End Learning of Decision Trees and Forests. International Journal of Computer Vision. 1-15
Cerrone, L, Zeilmann, A and Hamprecht, F A (2019). End-to-End Learned Random Walker for Seeded Image Segmentation. CVPR. Proceedings. 12559-12568
Kandemir, M, Zhang, C and Hamprecht, F A (2014). Empowering multiple instance histopathology cancer diagnosis by cell graphs. MICCAI. Proceedings. Springer. 8674 228-235PDF icon Technical Report (1.76 MB)
Andres, B, Kappes, J H, Köthe, U, Schnörr, C and Hamprecht, F A (2010). An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. Pattern Recognition, Proc.~32th DAGM SymposiumPDF icon Technical Report (218.43 KB)
Andres, B, Kappes, J H, Köthe, U, Schnörr, C and Hamprecht, F A (2010). An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. Pattern Recognition, Proc.~32th DAGM Symposium. 353-362
Lichy, M P, Bachert, P, Hamprecht, F A, Weber, M - A, Debus, J, Schulz-Ertner, D, Kauczor, H - U and Schlemmer, H - P (2006). Einsatz der 1H-MR-spektroskopischen Bildgebung in der Strahlentherapie: Cholin als Marker für die Bestimmung der relativen Wahrscheinlichkeit eines Tumorprogresses nach Bestrahlung glialer Hirntumoren. Zeitung für Röntgenforschung. 178 627-633
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)
Hader, S and Hamprecht, F A (2003). Efficient Density Clustering. Between Data Science and Applied Data Analysis. Springer. 39-48
Funke, J, Andres, B, Hamprecht, F A, Cardona, A and Cook, M (2012). Efficient Automatic 3D-Reconstruction of Branching Neurons from EM Data. CVPR 2012. Proceedings. 1004-1011PDF icon Technical Report (1.64 MB)
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Uhlmann, V, Haubold, C, Hamprecht, F A and Unser, M (2017). Diverse Shortest Paths for Bioimage Analysis. Bioinformatics. 1-3
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
Kausler, B X, Schiegg, M, Andres, B, Lindner, M, Köthe, U, Leitte, H, Wittbrodt, J, Hufnagel, L and Hamprecht, F A (2012). A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness. ECCV 2012. Proceedings. 7574 144-157PDF icon Technical Report (809.07 KB)
Kandemir, M, Feuchtinger, A, Walch, A and Hamprecht, F A (2014). Digital Pathology: Multiple instance learning can detect Barrett'scancer. ISBI. Proceedings. 1348-1351PDF icon Technical Report (2.86 MB)
Steen, J A J, Steen, H, Georgi, A, Parker, K C, Springer, M, Kirchner, M, Hamprecht, F A and Kirschner, M W (2008). Different Phosphorylation States of the Anaphase Promoting Complex in Response to Anti-Mitotic Drugs: A Quantitative Proteomic Analysis. Proceedings of the National Academy of Sciences. 105 6069-6074PDF icon Technical Report (173.02 KB)
Hamprecht, F A, Cohen, A J, Tozer, D J and Handy, N C (1998). Development and assessment of new exchange-correlation functionals. Journal of Chemical Physics. 109 6264-6271
Lou, X, Kirchner, M, Renard, B Y, Köthe, U, Graf, C, Lee, C, Steen, J A J, Steen, H, Mayer, M P and Hamprecht, F A (2010). Deuteration Distribution Estimation with Improved Sequence Coverage for HX/MS Experiments. Bioinformatics. 26(12) 1535-1541PDF icon Technical Report (518.01 KB)
Görlitz, L, Hamprecht, F A and Staudacher, M (2005). Detektion von Partikeln in Intensitätsbildern mit Hilfe eines morphologischen Skalenraumes. Robert-Bosch GmbH, University of Heidelberg
Decker, C and Hamprecht, F A (2014). Detecting individual body parts improves mouse behavior classification. Workshop on visual observation and analysis of Vertebrate And Insect Behavior (VAIB), 22nd International Conference on Pattern Recognition (ICPR). ProceedingsPDF icon Technical Report (1.48 MB)
Frank, M, Plaue, M and Hamprecht, F A (2009). Denoising of Continuous-Wave Time-Of-Flight Depth Images Using Confidence Measures. Optical Engineering. 48, 077003PDF icon Technical Report (2.5 MB)
Lou, X, Kaster, F O, Lindner, M, Kausler, B X, Köthe, U, Höckendorf, B, Wittbrodt, J, Jänicke, H and Hamprecht, F A (2011). DELTR: Digital Embryo Lineage Tree Reconstructor. Eighth IEEE International Symposium on Biomedical Imaging (ISBI). Proceedings. 1557-1560PDF icon Technical Report (1.44 MB)
Kandemir, M and Hamprecht, F A (2015). The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. NIPS. Proceedings. 44 145-159PDF icon Supplementary Material (223.39 KB)PDF icon Technical Report (2.58 MB)
Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Deep Active Learning with Adaptive Acquisition. IJCAI. Proceedings, in press
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Beier, T, Kröger, T, Kappes, J H, Köthe, U and Hamprecht, F A (2014). Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning. 2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014. http://dx.doi.org/10.1109/CVPR.2014.17PDF icon Technical Report (10.06 MB)
Peter, S, Diego, F, Hamprecht, F A and Nadler, B (2017). Cost-efficient Gradient Boosting. NIPS, poster
Maco, B, Holtmaat, A, Cantoni, M, Kreshuk, A, Straehle, C N, Hamprecht, F A and Knott, G W (2013). Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons. PloS one. 8 (2)PDF icon Technical Report (2.13 MB)
Schiegg, M, Hanslovsky, P, Kausler, B X, Hufnagel, L and Hamprecht, F A (2013). Conservation Tracking. ICCV 2013. Proceedings. 2928--2935PDF icon Technical Report (5.22 MB)
Hanselmann, M, Kirchner, M, Renard, B Y, Amstalden, E R, Glunde, K, Heeren, R M A and Hamprecht, F A (2008). Concise Representation of MS Images by Probabilistic Latent Semantic Analysis. Analytical Chemistry. 80 9649-9658PDF icon Technical Report (3.91 MB)
Kandemir, M and Hamprecht, F A (2014). Computer-aided diagnosis from weak supervision: A benchmarking study. Computerized Medical Imaging and Graphics. 42 44-50PDF icon Technical Report (4.28 MB)
Kirchner, M, Renard, B Y, Köthe, U, Pappin, D J, Hamprecht, F A, Steen, J A J and Steen, H (2010). Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments. Bioinformatics. 26 (1) 77-83PDF icon Technical Report (380.19 KB)
Menze, B H, Kelm, B Michael, Masuch, R, Himmelreich, U, Bachert, P, Petrich, W and Hamprecht, F A (2009). A Comparison of Random Forest and its Gini Importance with Standard Chemometric Methods for the Feature Selection and Classification of Spectral Data. BMC Bioinformatics. 10:213PDF icon Technical Report (675 KB)
Weber, C, Zechmann, C M, Kelm, B Michael, Zamecnik, R, Hendricks, D, Waldherr, R, Hamprecht, F A, Delorme, S, Bachert, P and Ikinger, U (2007). Comparison of correctness of manuel and automatic evaluation of MR-spectrum with prostrate cancer. Der Urologe. 46 1252
Kaster, F O, Weber, M - A and Hamprecht, F A (2011). Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations. LNCS. Springer, Heidelberg. LNCS 6533 74-85PDF icon Technical Report (544.56 KB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 1-30PDF icon Technical Report (1.5 MB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2014). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR. abs/1404.0533. http://hci.iwr.uni-heidelberg.de/opengm2/PDF icon Technical Report (3.32 MB)

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