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CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets

L. Bürgy, M. Weigert, G. Hatzopoulos, M. Minder, A. Journé, S. Rahi, and P. Gönczy. BMC bioinformatics, 24 (1): 1--10 (2023)

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Martin Potthast

Zero-shot Generative Large Language Models for Systematic Review Screening AutomationS. Wang, H. Scells, S. Zhuang, M. Potthast, B. Koopman, and G. Zuccon. Advances in Information Retrieval. 46th European Conference on IR Research (ECIR 2024), volume 14608 of Lecture Notes in Computer Science, page 403--420. Berlin Heidelberg New York, Springer, (March 2024)Identifying queries in instant search logsM. Fischer, K. Komlossy, B. Stein, M. Potthast, and M. Hagen. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, ACM, (July 2021)28. September - 2. Oktober 2020M. Völske, J. Bevendorff, J. Kiesel, B. Stein, M. Fröbe, M. Hagen, and M. Potthast. Gesellschaft für Informatik, Bonn, (2021)

Martin Lorenz

Visual Sampling Behavior Does not Explain Risk Perception: A Data-Driven xAI InvestigationM. Lorenz, J. Hilbert, P. Asteriou, P. Wintersberger, and P. Ebel. Proceedings of the 17th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, New York, NY, USA, Association for Computing Machinery, (2025)

Other publications of authors with the same name

A convolutional neural network segments yeast microscopy images with high accuracyN. Dietler, M. Minder, V. Gligorovski, A. Economou, D. Joly, A. Sadeghi, C. Chan, M. Koziński, M. Weigert, A. Bitbol and 1 other author(s). Nature Communications, 11 (1): 5723 (2020)Nuclei Instance Segmentation and Classification in Histopathology Images with StardistM. Weigert, and U. Schmidt. 2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC), page 1-4. (2022)Self-supervised dense representation learning for live-cell microscopy with time arrow predictionB. Gallusser, M. Stieber, and M. Weigert. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), (2023)Trackastra: Transformer-based cell tracking for live-cell microscopyB. Gallusser, and M. Weigert. European Conference on Computer Vision, page 467--484. Springer, (2024)CLIJ: GPU-accelerated image processing for everyoneR. Haase, L. Royer, P. Steinbach, D. Schmidt, A. Dibrov, U. Schmidt, M. Weigert, N. Maghelli, P. Tomancak, F. Jug and 1 other author(s). Nature methods, 17 (1): 5--6 (2020)Organelle-specific segmentation, spatial analysis, and visualization of volume electron microscopy datasetsA. Müller, D. Schmidt, L. Rieckert, M. Solimena, and M. Weigert. arXiv preprint arXiv:2303.03876, (2023)Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual FeaturesQ. Juppet, F. De Martino, E. Marcandalli, M. Weigert, O. Burri, M. Unser, C. Brisken, and D. Sage. Journal of Mammary Gland Biology and Neoplasia, (2021)CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasetsL. Bürgy, M. Weigert, G. Hatzopoulos, M. Minder, A. Journé, S. Rahi, and P. Gönczy. BMC bioinformatics, 24 (1): 1--10 (2023)BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage AnalysisW. Ouyang, F. Beuttenmueller, E. Gómez-de Mariscal, C. Pape, T. Burke, C. Garcia-López-de Haro, C. Russell, L. Moya-Sans, C. de-la Torre-Gutiérrez, D. Schmidt and 12 other author(s). bioRxiv, (2022)Modular segmentation, spatial analysis and visualization of volume electron microscopy datasetsA. Müller, D. Schmidt, J. Albrecht, L. Rieckert, M. Otto, L. Galicia Garcia, G. Fabig, M. Solimena, and M. Weigert. Nature Protocols, (2024)

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Disambiguation of "Weigert, Martin"

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CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets

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Martin Potthast

Martin Lorenz

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PUMA

Disambiguation of "Weigert, Martin"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets

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Martin Potthast

Martin Lorenz

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CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets