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O

B. H. Menze, Kelm, B. Michael, Splitthoff, N., Köthe, U., and Hamprecht, F. A., “On oblique random forests”, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2011. Proceedings., 2011, pp. 453-469.

Technical Report (665.33 KB)

F. O. Kaster, Merkel, B., Nix, O., and Hamprecht, F. A., “An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements”, Computer Science - Research and Development, vol. 26, pp. 65-85, 2011.

Technical Report (808.16 KB)

F. O. Kaster, Kassemeyer, S., Merkel, B., Nix, O., and Hamprecht, F. A., “An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements”, in Bildverarbeitung für die Medizin 2010 -- Algorithmen, Systeme, Anwendungen, 2010, pp. 97-101.

Technical Report (1.12 MB)

V. Ulman, 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., “An Objective Comparison of Cell Tracking Algorithms”, Nature Methods, vol. 14, no. 12, pp. 1141-1152, 2017.

Technical Report (4.24 MB)

N

J. Restle, Hissmann, M., and Hamprecht, F. A., “Nonparametric Smoothing of Height maps using ``Confidence'' values”, Optical Engineering, vol. 43, pp. 866-871, 2004.

Technical Report (1.07 MB)

B. Y. Renard, Kirchner, M., Steen, H., Steen, J. A. J., and Hamprecht, F. A., “NITPICK: Peak Identification for Mass Spectrometry Data”, BMC Bioinformatics, vol. 9, p. 355, 2008.

Technical Report (643.89 KB)

H. Eisele and Hamprecht, F. A., “A new approach for defect detection in X-ray CT images”, Pattern Recognition, vol. 2449. Springer, pp. 345-352, 2003.

Technical Report (398.88 KB)

N. Krasowki, Beier, T., Knott, G. W., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “Neuron Segmentation with High-Level Biological Priors”, IEEE Transactions on Medical Imaging, vol. 37, no. 4, 2017.

S. Trittler and Hamprecht, F. A., “Near-Optimum Sampling Design and an Efficient Algorithm for Single Tone Frequency Estimation”, Digital Signal Processing, vol. 19, pp. 628-639, 2009.

Technical Report (511.23 KB)

M

S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, ECCV. Proceedings. Springer, pp. 571-587, 2018.

S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11208 LNCS, pp. 571–587.

M. Hanselmann, Köthe, U., Renard, B. Y., Kirchner, M., Heeren, R. M. A., and Hamprecht, F. A., “Multivariate Watershed Segmentation of Compositional Data”, in Proceedings of the 15th International Conference on Discrete Geometry for Computer Imagery (DGCI), in press, 2009, vol. 5810, pp. 180-192.

Technical Report (1.25 MB)

B. H. Menze, Petrich, W., and Hamprecht, F. A., “Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy”, Analytical and Bioanalytical Chemistry, vol. 387, pp. 1801-1807, 2007.

Technical Report (283.47 KB)

C. N. Straehle, Kandemir, M., Köthe, U., and Hamprecht, F. A., “Multiple instance learning with response-optimized random forests”, in ICPR. Proceedings, 2014, pp. 3768 - 3773.

Technical Report (296.66 KB)

B. H. Menze and Hamprecht, F. A., “Multimodal Medical Image Analysis: from Visualization to Disease Modeling”, Zeitschrift für Med. Physik, vol. 1, pp. 1-2, 2010.

Technical Report (481.58 KB)

G. Urban, Bendszus, M., Hamprecht, F. A., and Kleesiek, J., “Multi-modal Brain Tumor Segmentation using Deep Convolutional NeuralNetworks”, in MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winningcontribution, 2014, pp. 31-35.

B. Jähne, Brocke, M., Eisele, H., Hader, S., Hamprecht, F. A., Happold, W., Raisch, F., and Restle, J., “Multidimensionale Bildverarbeitung in der Produktion”, QZ, vol. 47, p. 1154--1159, 2002.

T. Beier, 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., “Multicut brings automated neurite segmentation closer to human performance”, Nature Methods, vol. 14, no. 2, pp. 101-102, 2017.

P. J. Gee, Hamprecht, F. A., Schuler, L. D., van Gunsteren, W. F., Duchardt, E., Schwalbe, H., Albert, M., and Seebach, D., “A molecular dynamics simulation study of the conformational preferences of oligo-(3- hydroxyalcanoic acids) in chloroform solution”, Helv. Chim. Acta, vol. 85, pp. 618-632, 2002.

B. H. Menze, Kelm, B. Michael, Weber, M. - A., Bachert, P., and Hamprecht, F. A., “Mimicking the human expert: pattern recognition for an automated assessment of data quality in MRSI”, Magnetic Resonance in Medicine, vol. 59, pp. 1457-1466, 2008.

Technical Report (1.45 MB)

M. Kirchner, Steen, J. A. J., Hamprecht, F. A., and Steen, H., “MGFp: An Open Mascot Generic Format Parser Library Implementation”, Journal of Proteome Research, vol. 9 (5), p. 27622763, 2010.

Technical Report (125.18 KB)

M. Staudacher, Hamprecht, F. A., and Görlitz, L., “Method for processing an intensity image of a microscope”, Patent, Patent Number: WO2008034721A1, 2008.

Technical Report (39.81 KB)

M. Kandemir, Hamprecht, F. A., Wojek, C., and Schmidt, U., “Maschinelles Lernen”, Patent, Patent Number WO2017032775A1, 2017.

Technical Report (317.04 KB)

B. H. Menze, Kelm, B. Michael, Heck, D., Lichy, M. P., and Hamprecht, F. A., “Machine-based rejection of low quality spectra and estimation of brain tumor probabilities from magnetic resonance spectroscopic images”, in Bildverarbeitung für die Medizin, 2006, pp. 31-36.

Technical Report (672.84 KB)

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E. Kirschbaum, Haußmann, M., Wolf, S., Sonntag, H., Schneider, J., Elzoheiry, S., Kann, O., Durstewitz, D., and Hamprecht, F. A., “LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos”, ICLR. Proceedings. 2019.

C. Sommer, Fiaschi, L., Hamprecht, F. A., and Gerlich, D., “Learning-based Mitotic Cell Detection in Histopathological Images”, ICPR 2012. Proceedings, pp. 2306-2309, 2012.

Technical Report (1.96 MB)

J. Funke, Hamprecht, F. A., and Zhang, C., “Learning to Segment: Training Hierarchical Segmentation under a Topological Loss”, in MICCAI. Proceedings, Part III, 2015, vol. 9351, pp. 268-275.

Technical Report (2.92 MB)

T. Kröger, Mikula, S., Denk, W., Köthe, U., and Hamprecht, F. A., “Learning to Segment Neurons with Non-local Quality Measures”, in MICCAI 2013. Proceedings, part II, 2013, vol. 8150, pp. 419-427.

Technical Report (2.87 MB)

X. Lou and Hamprecht, F. A., “Learning to Segment Dense Cell Nuclei with Shape Prior”, CVPR 2012. Proceedings, pp. 1012-1018, 2012.

Technical Report (2.66 MB)

L. Fiaschi, Nair, R., Köthe, U., and Hamprecht, F. A., “Learning to Count with Regression Forest and Structured Labels”, ICPR 2012. Proceedings, pp. 2685-2688, 2012.

Technical Report (3.66 MB)

M. Weiler, Hamprecht, F. A., and Storath, M., “Learning Steerable Filters for Rotation Equivariant CNNs”, CVPR. Proceedings. pp. 849-858, 2018.

Technical Report (1.35 MB)

F. Diego and Hamprecht, F. A., “Learning Multi-Level Sparse Representation for Identifying Neuronal Activity”, in Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS). Book of Abstracts., 2013.

Technical Report (1.05 MB)

F. Diego and Hamprecht, F. A., “Learning Multi-Level Sparse Representation”, in NIPS. Proceedings, 2013.

Technical Report (2.79 MB)

M. Schiegg, Diego, F., and Hamprecht, F. A., “Learning Diverse Models: The Coulomb Structured Support Vector Machine”, ECCV. Proceedings, vol. LNCS 9907. Springer, pp. 585-599, 2016.

Technical Report (2.54 MB)

S. Wolf, Schott, L., Köthe, U., and Hamprecht, F. A., “Learned Watershed: End-to-End Learning of Seeded Segmentation”, ICCV. pp. 2030-2038, 2017.

Technical Report (3.76 MB)

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