/** * Implements hook_help(). * * Displays help and module information. * * @param path * Which path of the site we're using to display help * @param arg * Array that holds the current path as returned from arg() function */ function current_posts_help($path, $arg) { switch ($path) { case "admin/help#current_posts": return t("Displays links to nodes created on this date"); break; } } function spamspan($text = '', $settings = array()) { //apply default settings $info = filter_get_filters(); $defaults = $info['spamspan']['default settings']; //create a dummy filter object so we can apply the settings $filter = new stdClass(); $filter->settings = $settings + $defaults; return _spamspan_filter_process($text, $filter); } function hook_entity_view_alter(&$build, $type) { if ($build['#view_mode'] == 'full' && isset($build['an_additional_field'])) { // Change its weight. $build['an_additional_field']['#weight'] = -10; // Add a #post_render callback to act on the rendered HTML of the entity. $build['#post_render'][] = 'my_module_node_post_render'; } } function module_invoke($module, $hook) { $args = func_get_args(); // Remove $module and $hook from the arguments. unset($args[0], $args[1]); if (module_hook($module, $hook)) { return call_user_func_array($module . '_' . $hook, $args); } } /** * Implements hook_views_api(). */ function safemail_inviews_views_api() { return array( 'api' => '3', 'path' => drupal_get_path('module', 'safemail_inviews') . '/includes', ); } Publications | Page 3 | Heidelberg Collaboratory for Image Processing (HCI)

Publications

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O
Menze, B H, Kelm, B Michael, Splitthoff, N, Köthe, U and Hamprecht, F A (2011). On oblique random forests. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2011. Proceedings. Springer. 453-469PDF icon Technical Report (665.33 KB)
Kaster, F O, Merkel, B, Nix, O and Hamprecht, F A (2011). An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements. Computer Science - Research and Development. 26 65-85PDF icon Technical Report (808.16 KB)
Kaster, F O, Kassemeyer, S, Merkel, B, Nix, O and Hamprecht, F A (2010). An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements. Bildverarbeitung für die Medizin 2010 -- Algorithmen, Systeme, Anwendungen. Springer. 97-101PDF icon Technical Report (1.12 MB)
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 1141-1152PDF icon Technical Report (4.24 MB)
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Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. ECCV. Proceedings. Springer. 571-587
Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 571–587. http://arxiv.org/abs/1904.12654
Hanselmann, M, Köthe, U, Renard, B Y, Kirchner, M, Heeren, R M A and Hamprecht, F A (2009). Multivariate Watershed Segmentation of Compositional Data. Proceedings of the 15th International Conference on Discrete Geometry for Computer Imagery (DGCI), in press. Springer. 5810 180-192PDF icon Technical Report (1.25 MB)
Menze, B H, Petrich, W and Hamprecht, F A (2007). Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy. Analytical and Bioanalytical Chemistry. 387 1801-1807PDF icon Technical Report (283.47 KB)
Straehle, C N, Kandemir, M, Köthe, U and Hamprecht, F A (2014). Multiple instance learning with response-optimized random forests. ICPR. Proceedings. 3768 - 3773PDF icon Technical Report (296.66 KB)
Menze, B H and Hamprecht, F A (2010). Multimodal Medical Image Analysis: from Visualization to Disease Modeling. Zeitschrift für Med. Physik. 1 1-2PDF icon Technical Report (481.58 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
Jähne, B, Brocke, M, Eisele, H, Hader, S, Hamprecht, F A, Happold, W, Raisch, F and Restle, J (2002). Multidimensionale Bildverarbeitung in der Produktion. QZ. 47 1154--1159. http://www.qz-online.de/qz-zeitschrift/archiv/artikel/multidimensionale-bildverarbeitung-in-der-produktion-fuer-anspruchsvolle-338129.html
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
Gee, P J, Hamprecht, F A, Schuler, L D, van Gunsteren, W F, Duchardt, E, Schwalbe, H, Albert, M and Seebach, D (2002). A molecular dynamics simulation study of the conformational preferences of oligo-(3- hydroxyalcanoic acids) in chloroform solution. Helv. Chim. Acta. 85 618-632
Menze, B H, Kelm, B Michael, Weber, M - A, Bachert, P and Hamprecht, F A (2008). Mimicking the human expert: pattern recognition for an automated assessment of data quality in MRSI. Magnetic Resonance in Medicine. 59 1457-1466PDF icon Technical Report (1.45 MB)
Kirchner, M, Steen, J A J, Hamprecht, F A and Steen, H (2010). MGFp: An Open Mascot Generic Format Parser Library Implementation. Journal of Proteome Research. 9 (5) 27622763PDF icon Technical Report (125.18 KB)
Staudacher, M, Hamprecht, F A and Görlitz, L (2008). Method for processing an intensity image of a microscope. Patent, Patent Number: WO2008034721A1PDF icon Technical Report (39.81 KB)
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Maschinelles Lernen. Patent, Patent Number WO2017032775A1PDF icon Technical Report (317.04 KB)
Menze, B H, Kelm, B Michael, Heck, D, Lichy, M P and Hamprecht, F A (2006). Machine-based rejection of low quality spectra and estimation of brain tumor probabilities from magnetic resonance spectroscopic images. Bildverarbeitung für die Medizin. 31-36PDF icon Technical Report (672.84 KB)
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Kirschbaum, E, Haußmann, M, Wolf, S, Sonntag, H, Schneider, J, Elzoheiry, S, Kann, O, Durstewitz, D and Hamprecht, F A (2019). LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR. Proceedings
Sommer, C, Fiaschi, L, Hamprecht, F A and Gerlich, D (2012). Learning-based Mitotic Cell Detection in Histopathological Images. ICPR 2012. Proceedings. 2306-2309PDF icon Technical Report (1.96 MB)
Funke, J, Hamprecht, F A and Zhang, C (2015). Learning to Segment: Training Hierarchical Segmentation under a Topological Loss. MICCAI. Proceedings, Part III. Springer. 9351 268-275PDF icon Technical Report (2.92 MB)
Kröger, T, Mikula, S, Denk, W, Köthe, U and Hamprecht, F A (2013). Learning to Segment Neurons with Non-local Quality Measures. MICCAI 2013. Proceedings, part II. Springer. 8150 419-427PDF icon Technical Report (2.87 MB)
Lou, X and Hamprecht, F A (2012). Learning to Segment Dense Cell Nuclei with Shape Prior. CVPR 2012. Proceedings. 1012-1018PDF icon Technical Report (2.66 MB)
Fiaschi, L, Nair, R, Köthe, U and Hamprecht, F A (2012). Learning to Count with Regression Forest and Structured Labels. ICPR 2012. Proceedings. 2685-2688PDF icon Technical Report (3.66 MB)
Weiler, M, Hamprecht, F A and Storath, M (2018). Learning Steerable Filters for Rotation Equivariant CNNs. CVPR
Diego, F and Hamprecht, F A (2013). Learning Multi-Level Sparse Representation for Identifying Neuronal Activity. Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS). Book of AbstractsPDF icon Technical Report (1.05 MB)
Diego, F and Hamprecht, F A (2013). Learning Multi-Level Sparse Representation. NIPS. Proceedings. http://papers.nips.cc/paper/5076-learning-multi-level-sparse-representationsPDF icon Technical Report (2.79 MB)
Schiegg, M, Diego, F and Hamprecht, F A (2016). Learning Diverse Models: The Coulomb Structured Support Vector Machine. ECCV. Proceedings. Springer. LNCS 9907 585-599PDF icon Technical Report (2.54 MB)
Wolf, S, Schott, L, Köthe, U and Hamprecht, F A (2017). Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV. 2030-2038PDF icon Technical Report (3.76 MB)

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