Demo Data from EDBT 2026 / reviewers in the wild / expert
Michal Kozubek 0001
dblp:61/1228
· DBLP profile ↗
9ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0001-7902-589XORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 5Graphics, computer vision, multimedia, augmented reality and games · 4 · 1 since 2021Artificial intelligence and machine learning · 2 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
4 papers |
Medical and health informatics · 53% Bioinformatics and computational biology · 47% | |
| Artificial intelligence
1 paper |
Deep learning architectures and training · 100% |
Topics — the 6 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics › medical imaging
medical image analysis |
0.8 | 2 | 2023 | Why is the Winner the Best? · CVPR 2023 A benchmark for comparison of cell tracking algorithms · Bioinform. 2014 |
Bioinformatics and computational biology › bioimage informatics
bioimage analysis |
0.4 | 1 | 2019 | CytoPacq: a web-interface for simulating multi-dimensional cell imaging · Bioinform. 2019 |
Medical and health informatics
computational pathology |
0.3 | 1 | 2017 | Image-based surrogate biomarkers for molecular subtypes of colorectal cancer · Bioinform. 2017 |
Bioinformatics and computational biology › bioimage informatics
cell tracking |
0.2 | 1 | 2014 | A benchmark for comparison of cell tracking algorithms · Bioinform. 2014 |
Image and video processing
image registration |
0.1 | 1 | 2006 | Fast point-based 3-D alignment of live cells · IEEE Trans. Image Process. 2006 |
Bioinformatics and computational biology › bioimage informatics
cell segmentation |
0.1 | 1 | 2014 | A benchmark for comparison of cell tracking algorithms · Bioinform. 2014 |
Methods — techniques the papers use, named apart from their topics
statistical analysis · 1.3web interface · 0.4fluorescence microscopy simulation · 0.4support vector machine · 0.3dictionary-based representation · 0.3convolutional neural network · 0.3performance evaluation · 0.2benchmarking · 0.2point pattern matching · 0.1least-squares transformation estimation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Why is the Winner the Best?abstractInternational benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Tizabi, Fabian Isensee, Tim Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David Gage Ellis, Sandy Engelhardt, Melanie Ganz-Benjaminsen, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek 0001, Jianning Li 0002, Hongwei Li 0004, Jun Ma 0016, Carlos Martín-Isla, Bjoern Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, Martin Wagner 0001, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Qi Dou 0001, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo J. Kuijf, M. Luu, Tomaz Martincic, Pedro Morais, Mohamed A. Naser, Bruno Oliveira 0002, David Owen 0001, S. Pang, Szymon Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, Numan Saeed, Adam J. Shephard, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, Sen Yang 0006, Klaus H. Maier-Hein, Paul F. Jaeger, Annette Kopp-Schneider, Lena Maier-Hein |
CVPR | 39 |
| 2020 | BIAS: Transparent reporting of biomedical image analysis challengesabstractThe number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common data sets, typically to identify the best method for a given problem. Recent research, however, revealed that common practice related to challenge reporting does not allow for adequate interpretation and reproducibility of results. To address the discrepancy between the impact of challenges and the quality (control), the Biomedical Image Analysis ChallengeS (BIAS) initiative developed a set of recommendations for the reporting of challenges. The BIAS statement aims to improve the transparency of the reporting of a biomedical image analysis challenge regardless of field of application, image modality or task category assessed. This article describes how the BIAS statement was developed and presents a checklist which authors of biomedical image analysis challenges are encouraged to include in their submission when giving a paper on a challenge into review. The purpose of the checklist is to standardize and facilitate the review process and raise interpretability and reproducibility of challenge results by making relevant information explicit. Lena Maier-Hein, Annika Reinke, Michal Kozubek 0001, Anne L. Martel, Tal Arbel, Matthias Eisenmann, Allan Hanbury, Pierre Jannin, Henning Müller, Sinan Onogur, Julio Saez-Rodriguez, Bram van Ginneken, Annette Kopp-Schneider, Bennett A. Landman |
Medical Image Anal. | 3 |
| 2019 | CytoPacq: a web-interface for simulating multi-dimensional cell imagingabstractMOTIVATION: Objective assessment of bioimage analysis methods is an essential step towards understanding their robustness and parameter sensitivity, calling for the availability of heterogeneous bioimage datasets accompanied by their reference annotations. Because manual annotations are known to be arduous, highly subjective and barely reproducible, numerous simulators have emerged over past decades, generating synthetic bioimage datasets complemented with inherent reference annotations. However, the installation and configuration of these tools generally constitutes a barrier to their widespread use. RESULTS: We present a modern, modular web-interface, CytoPacq, to facilitate the generation of synthetic benchmark datasets relevant for multi-dimensional cell imaging. CytoPacq poses a user-friendly graphical interface with contextual tooltips and currently allows a comfortable access to various cell simulation systems of fluorescence microscopy, which have already been recognized and used by the scientific community, in a straightforward and self-contained form. AVAILABILITY AND IMPLEMENTATION: CytoPacq is a publicly available online service running at https://cbia.fi.muni.cz/simulator. More information about it as well as examples of generated bioimage datasets are available directly through the web-interface. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. David Wiesner, David Svoboda, Martin Maska, Michal Kozubek 0001 |
Bioinform. | 4 |
| 2018 | How to Exploit Weaknesses in Biomedical Challenge Design and Organization
Annika Reinke, Matthias Eisenmann, Sinan Onogur, Marko Stankovic 0002, Patrick Godau, Peter M. Full, Hrvoje Bogunovic, Bennett A. Landman, Oskar Maier, Bjoern Menze, Gregory C. Sharp, Korsuk Sirinukunwattana, Stefanie Speidel, Fons van der Sommen, Guoyan Zheng, Henning Müller, Michal Kozubek 0001, Tal Arbel, Andrew P. Bradley, Pierre Jannin, Annette Kopp-Schneider, Lena Maier-Hein |
MICCAI (4) | 17 |
| 2017 | Image-based surrogate biomarkers for molecular subtypes of colorectal cancerabstractMOTIVATION: Whole genome expression profiling of large cohorts of different types of cancer led to the identification of distinct molecular subcategories (subtypes) that may partially explain the observed inter-tumoral heterogeneity. This is also the case of colorectal cancer (CRC) where several such categorizations have been proposed. Despite recent developments, the problem of subtype definition and recognition remains open, one of the causes being the intrinsic heterogeneity of each tumor, which is difficult to estimate from gene expression profiles. However, one of the observations of these studies indicates that there may be links between the dominant tumor morphology characteristics and the molecular subtypes. Benefiting from a large collection of CRC samples, comprising both gene expression and histopathology images, we investigated the possibility of building image-based classifiers able to predict the molecular subtypes. We employed deep convolutional neural networks for extracting local descriptors which were then used for constructing a dictionary-based representation of each tumor sample. A set of support vector machine classifiers were trained to solve different binary decision problems, their combined outputs being used to predict one of the five molecular subtypes. RESULTS: A hierarchical decomposition of the multi-class problem was obtained with an overall accuracy of 0.84 (95%CI=0.79-0.88). The predictions from the image-based classifier showed significant prognostic value similar to their molecular counterparts. CONTACT: [email protected]. AVAILABILITY AND IMPLEMENTATION: Source code used for the image analysis is freely available from https://github.com/higex/qpath . SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Vlad Popovici, Eva Budinska, Ladislav Dusek, Michal Kozubek 0001, Fred Bosman |
Bioinform. | 4 |
| 2015 | Quantification of the 3D collagen network geometry in confocal reflection microscopyabstractThe geometry of 3D collagen networks is a key factor that influences the behavior of live cells within extracellular matrices. This paper presents a hybrid, two-step method for fully automatic quantification of the 3D collagen network geometry at fiber resolution in confocal reflection microscopy images. First, a coarse binary mask of the entire network is obtained using steerable filtering and local Otsu thresholding. Second, individual collagen fibers are reconstructed by tracing maximum ridges in the Euclidean distance map of the binary mask. The proposed method, validated in a novel framework using 3D collagen gels with various collagen and Matrigel concentrations, reveal that Matrigel affects the collagen network geometry by decreasing the network pore size while preserving the fiber length and fiber persistence length. Martin Maska, Cristina Ederra, Javier Fernández-Marqués, Arrate Muñoz-Barrutia, Michal Kozubek 0001, Carlos Ortiz-de-Solorzano |
ICIP | 5 |
| 2014 | A benchmark for comparison of cell tracking algorithmsabstractMOTIVATION: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. RESULTS: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. AVAILABILITY AND IMPLEMENTATION: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge. Martin Maska, Vladimír Ulman, David Svoboda, Pavel Matula, Petr Matula, Cristina Ederra, Ainhoa Urbiola, Tomás España, Subramanian Venkatesan 0001, Deepak M. W. Balak, Pavel Karas, Tereza Bolcková, Markéta Streitová, Craig Carthel, Stefano Coraluppi, Nathalie Harder, Karl Rohr, Klas E. G. Magnusson, Joakim Jaldén, Helen M. Blau, Oleh Dzyubachyk, Pavel Krízek, Guy M. Hagen, David Pastor-Escuredo, Daniel Jimenez-Carretero, María J. Ledesma-Carbayo, Arrate Muñoz-Barrutia, Erik Meijering, Michal Kozubek 0001, Carlos Ortiz-de-Solorzano |
Bioinform. | 29 |
| 2012 | Smooth Chan-Vese segmentation via graph cuts
Ondrej Danek, Pavel Matula, Martin Maska, Michal Kozubek 0001 |
Pattern Recognit. Lett. | 4 |
| 2006 | Fast point-based 3-D alignment of live cellsabstractTypical time intervals between acquisitions of three-dimensional (3-D) images of the same cell in live cell imaging are in the orders of minutes. In the meantime, the live cell can move in a water basin on the stage. This movement can hamper the studies of intranuclear processes. We propose a fast point-based image registration method for the suppression of the movement of a cell as a whole in the image data. First, centroids of certain intracellular objects are computed for each image in a time-lapse series. Then, a matching between the centroids, which have the maximal number of pairs, is sought between consecutive point sets by a 3-D extension of a two-dimensional fast point pattern matching method, which is invariant to rotation, translation, local distortion, and extra/missing points. The proposed 3-D extension assumes rotations only around the z axis to retain the complexity of the original method. The final step involves computing the optimal fully 3-D transformation between images from corresponding points in the least-squares manner. The robustness of the method was evaluated on generated data. The results of the simulations show that the method is very precise and its correctness can be estimated. This article also presents two practical application examples, namely the registration of images of HP1 domains and the registration of images of telomeres. More than 97% of time-consecutive images were successfully registered. The results show that the method is very well suited to live cell imaging. Petr Matula, Michal Kozubek 0001, Vladimír Dvorák |
IEEE Trans. Image Process. | 2 |