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2013

M. Schiegg, Hanslovsky, P., Kausler, B. X., Hufnagel, L., and Hamprecht, F. A., “Conservation Tracking”, in ICCV 2013. Proceedings, 2013, p. 2928--2935.

Technical Report (5.22 MB)

B. Maco, Holtmaat, A., Cantoni, M., Kreshuk, A., Straehle, C. N., Hamprecht, F. A., and Knott, G. W., “Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons”, PloS one, vol. 8 (2), 2013.

Technical Report (2.13 MB)

L. Fiaschi, Grosser, K. - H., Afonso, B., Zlatic, M., and Hamprecht, F. A., “Keeping Count: Leveraging Temporal Context to Count Heavily Overlapping Objects”, ISBI 2013.Proceedings, pp. 656-659, 2013.

Technical Report (711.68 KB)

C. N. Straehle, Peter, S., Köthe, U., and Hamprecht, F. A., “K-smallest Spanning Tree Segmentations”, in German Conference on Pattern Recognition (DAGM/GCPR). Proceedings, 2013, pp. 375-384.

Technical Report (1.18 MB)

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

Technical Report (2.79 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)

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)

C. N. Straehle, Köthe, U., and Hamprecht, F. A., “Weakly supervised learning of image partitioning using decision trees with structured split criteria”, in ICCV 2013. Proceedings, 2013, pp. 1849-1856.

Technical Report (5.97 MB)

2014

X. Lou, Schiegg, M., and Hamprecht, F. A., “Active Structured Learning for Cell Tracking: Algorithm, Framework and Usability”, IEEE Transactions on Medical Imaging, vol. 33 (4), pp. 849-860, 2014.

Technical Report (6.84 MB)

T. Kröger, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Asymmetric Cuts: Joint Image Labeling and Partitioning”, in Pattern Recognition - 36th German Conference, {GCPR} 2014, Münster, Germany, September 2-5, 2014, Proceedings, 2014.

Technical Report (3.46 MB)

T. Kröger, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Asymmetric Cuts: Joint Image Labeling and Partitioning”, in 36th German Conference on Pattern Recognition, 2014.

B. F. Tek, Kröger, T., Mikula, S., and Hamprecht, F. A., “Automated Cell Nucleus Detection for Large-Volume Electron Microscopy of Neural Tissue”, in ISBI. Proceedings, 2014, pp. 69-72.

Technical Report (533.92 KB)

A. Kreshuk, Köthe, U., Pax, E., Bock, D. D., and Hamprecht, F. A., “Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks”, PLoS ONE, vol. 9, p. 2, 2014.

Technical Report (16.66 MB)

C. Zhang, Yarkony, J., and Hamprecht, F. A., “Cell detection and segmentation using correlation clustering”, in MICCAI. Proceedings, 2014, pp. 9-16.

Technical Report (8.06 MB)

J. H. Kappes, 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., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, CoRR, 2014.

J. H. Kappes, 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., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, CoRR, vol. abs/1404.0533, 2014.

Technical Report (3.32 MB)

M. Kandemir and Hamprecht, F. A., “Computer-aided diagnosis from weak supervision: A benchmarking study”, Computerized Medical Imaging and Graphics, vol. 42, pp. 44-50, 2014.

Technical Report (4.28 MB)

T. Beier, Kröger, T., Kappes, J. H., Köthe, U., and Hamprecht, F. A., “Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning”, in 2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014, 2014.

Technical Report (10.06 MB)

C. Decker and Hamprecht, F. A., “Detecting individual body parts improves mouse behavior classification”, in Workshop on visual observation and analysis of Vertebrate And Insect Behavior (VAIB), 22nd International Conference on Pattern Recognition (ICPR). Proceedings, 2014.

Technical Report (1.48 MB)

M. Kandemir, Feuchtinger, A., Walch, A., and Hamprecht, F. A., “Digital Pathology: Multiple instance learning can detect Barrett'scancer”, ISBI. Proceedings. pp. 1348-1351, 2014.

Technical Report (2.86 MB)

M. Kandemir, Zhang, C., and Hamprecht, F. A., “Empowering multiple instance histopathology cancer diagnosis by cell graphs”, in MICCAI. Proceedings, 2014, vol. 8674, pp. 228-235.

Technical Report (1.76 MB)

M. Kandemir, Rubio, J. C., Schmidt, U., Wojek, C., Welbl, J., Ommer, B., and Hamprecht, F. A., “Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures”, in Medical Image Computing and Computer-Assisted Intervention, 2014, p. 154--161.

Technical Report (2 MB)

M. Kandemir, Rubio, J. C., Schmidt, U., Welbl, J., Ommer, B., and Hamprecht, F. A., “Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures”, in MICCAI. Proceedings, 2014, pp. 154-161.

Paper (2 MB)

R. Lindner, Lou, X., Reinstein, J., Shoeman, R. L., Hamprecht, F. A., and Winkler, A., “Hexicon 2: Automated Processing of Hydrogen-Deuterium Exchange Mass Spectrometry Data with Improved Deuteration Distribution Estimation”, Journal of The American Society for Mass Spectrometry, vol. 25, pp. 1018-1028, 2014.

Technical Report (2.1 MB)

J. Kleesiek, Biller, A., Urban, G., Köthe, U., Bendszus, M., and Hamprecht, F. A., “ilastik for Multi-modal Brain Tumor Segmentation”, in MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, 3rdplace, 2014, pp. 12-17.

Technical Report (405.91 KB)

M. Kandemir and Hamprecht, F. A., “Instance Label Prediction by Dirichlet Process Multiple Instance Learning”, in UAI. Proceedings, 2014.

Technical Report (4.26 MB)

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.

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)

J. Yarkony, Beier, T., Baldi, P., and Hamprecht, F. A., “Parallel Multicut Segmentation via Dual Decomposition”, in New Frontiers in Mining Complex Patterns - Third International Workshop, {NFMCP} 2014, Held in Conjunction with {ECML-PKDD} 2014, Nancy, France, September 19, 2014, Revised Selected Papers, 2014.

B. Maco, Cantoni, M., Holtmaat, A., Kreshuk, A., Hamprecht, F. A., and Knott, G. W., “Semiautomated Correlative 3D Electron Microscopy of In Vivo Imaged Axons and Dendrites”, Nature Protocols, vol. 9, pp. 1354-1366, 2014.

Technical Report (2.01 MB)

A. Drory, Haubold, C., Avidan, S., and Hamprecht, F. A., “Semi-Global Matching: A Principled Derivation in Terms of Message Passing”, in GCPR. Proceedings, 2014, pp. 43-53.

Technical Report (2.6 MB)

U. Köthe, Herrmannsdörfer, F., Kats, I., and Hamprecht, F. A., “SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy”, Histochemistry and Cell Biology, vol. 141, pp. 613-627, 2014.

Technical Report (2.29 MB)

F. Diego and Hamprecht, F. A., “Sparse Space-Time Deconvolution for Calcium Image Analysis”, in NIPS. Proceedings, 2014, pp. 64-72.

Technical Report (5.27 MB)

X. Lou, Kloft, M., Rätsch, G., and Hamprecht, F. A., “Structured Learning from Cheap Data”, Advanced Structured Prediction. The MIT Press, 2014.

Technical Report (8.35 MB)

L. Fiaschi, Diego, F., Grosser, K. - H., Schiegg, M., Köthe, U., Zlatic, M., and Hamprecht, F. A., “Tracking indistinguishable translucent objects over time using weakly supervised structured learning”, in CVPR. Proceedings, 2014, pp. 2736 - 2743.

Technical Report (1.47 MB)

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