Lou, X, Schiegg, M and Hamprecht, F A (2014). Active Structured Learning for Cell Tracking: Algorithm, Framework and Usability. IEEE Transactions on Medical Imaging. 33 (4) 849-860 Technical Report (6.84 MB) |
Kröger, T, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2014). Asymmetric Cuts: Joint Image Labeling and Partitioning. Pattern Recognition - 36th German Conference, {GCPR} 2014, Münster, Germany, September 2-5, 2014, Proceedings. http://dx.doi.org/10.1007/978-3-319-11752-2_16 Technical Report (3.46 MB) |
Decker, C (2014). Automated Animal Behavior Classification. University of Heidelberg |
Tek, B F, Kröger, T, Mikula, S and Hamprecht, F A (2014). Automated Cell Nucleus Detection for Large-Volume Electron Microscopy of Neural Tissue. ISBI. Proceedings. 69-72 Technical Report (533.92 KB) |
Kreshuk, A, Köthe, U, Pax, E, Bock, D D and Hamprecht, F A (2014). Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks. PLoS ONE. 9 2 Technical Report (16.66 MB) |
Welbl, J (2014). Casting Random Forests as Artificial Neural Networks (and Profiting from It). GCPR. Proceedings. 765-771 Technical Report (376.24 KB) |
Zhang, C, Yarkony, J and Hamprecht, F A (2014). Cell detection and segmentation using correlation clustering. MICCAI. Proceedings. Springer. 9-16 Technical Report (8.06 MB) |
Kandemir, M and Hamprecht, F A (2014). Computer-aided diagnosis from weak supervision: A benchmarking study. Computerized Medical Imaging and Graphics. 42 44-50 Technical Report (4.28 MB) |
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.2014.17 Technical Report (10.06 MB) |
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). Proceedings Technical Report (1.48 MB) |
Kandemir, M, Feuchtinger, A, Walch, A and Hamprecht, F A (2014). Digital Pathology: Multiple instance learning can detect Barrett'scancer. ISBI. Proceedings. 1348-1351 Technical Report (2.86 MB) |
Kandemir, M, Zhang, C and Hamprecht, F A (2014). Empowering multiple instance histopathology cancer diagnosis by cell graphs. MICCAI. Proceedings. Springer. 8674 228-235 Technical Report (1.76 MB) |
Kandemir, M, Rubio, J C, Schmidt, U, Welbl, J, Ommer, B and Hamprecht, F A (2014). Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. MICCAI. Proceedings. Springer. 154-161 Paper (2 MB) |
Kandemir, M, Rubio, J C, Schmidt, U, Wojek, C, Welbl, J, Ommer, B and Hamprecht, F A (2014). Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. Medical Image Computing and Computer-Assisted Intervention. Springer. 154--161 Technical Report (2 MB) |
Beier, T (2014). Graph Based Image Analysis. University of Heidelberg |
Lindner, R, Lou, X, Reinstein, J, Shoeman, R L, Hamprecht, F A and Winkler, A (2014). Hexicon 2: Automated Processing of Hydrogen-Deuterium Exchange Mass Spectrometry Data with Improved Deuteration Distribution Estimation. Journal of The American Society for Mass Spectrometry. 25 1018-1028 Technical Report (2.1 MB) |
Yarkony, J, Zhang, C and Fowlkes, C C (2014). Hierarchical Planar Correlation Clustering for Cell Segmentation. EMMCVPR. Proceedings. Springer. 8932 492-504 Technical Report (548.12 KB) |
Kleesiek, J, Biller, A, Urban, G, Köthe, U, Bendszus, M and Hamprecht, F A (2014). ilastik for Multi-modal Brain Tumor Segmentation. MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, 3rdplace. 12-17 Technical Report (405.91 KB) |
Blumenthal, F (2014). Information-Geometric Optimization For Image Segmentation. University of Heidelberg |
Kandemir, M and Hamprecht, F A (2014). Instance Label Prediction by Dirichlet Process Multiple Instance Learning. UAI. Proceedings Technical Report (4.26 MB) |
Straehle, C N (2014). Interactive Segmentation, Uncertainty and Learning. University of Heidelberg |
Kröger, (2014). Learning-based Segmentation for Connectomics. University of Heidelberg |
Kappes, J H, Beier, T and Schnörr, C (2014). MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves. Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}. http://dx.doi.org/10.1007/978-3-319-16181-5_37 Technical Report (557.49 KB) |
Urban, G, Bendszus, M, Hamprecht, F A and Kleesiek, J (2014). Multi-modal Brain Tumor Segmentation using Deep Convolutional NeuralNetworks. MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winningcontribution. 31-35 |
Kandemir, M, Klami, A, Gonen, M, Vetek, A and Kaski, S (2014). Multi-task and multi-view learning of user state. Neurocomputing. 139 97-106 |