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CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

S. Graham, Q. Vu, M. Jahanifar, M. Weigert, U. Schmidt, W. Zhang, J. Zhang, S. Yang, J. Xiang, X. Wang, and others. arXiv preprint arXiv:2303.06274, (2023)
DOI: https://doi.org/10.1016/j.media.2023.103047

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Content-Aware Image Restoration: Pushing the Limits of Fluorescence MicroscopyM. Weigert, U. Schmidt, T. Boothe, A. Müller, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, S. Culley and 11 other author(s). Nature Methods, 15 (12): 1090--1097 (2018)An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnosticsS. Schmell, F. Zakrzewski, W. de Back, M. Weigert, U. Schmidt, T. Wenke, S. Zeugner, R. Mantey, C. Sperling, I. Roeder and 3 other author(s). Medical Imaging with Deep Learning (MIDL), (2020)Star-convex Polyhedra for 3D Object Detection and Segmentation in MicroscopyM. Weigert, U. Schmidt, R. Haase, K. Sugawara, and G. Myers. WACV, (2020)CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and countingS. Graham, Q. Vu, M. Jahanifar, M. Weigert, U. Schmidt, W. Zhang, J. Zhang, S. Yang, J. Xiang, X. Wang and 1 other author(s). Medical image analysis, (2024)CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and CountingS. Graham, Q. Vu, M. Jahanifar, M. Weigert, U. Schmidt, W. Zhang, J. Zhang, S. Yang, J. Xiang, X. Wang and 1 other author(s). arXiv preprint arXiv:2303.06274, (2023)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)Removing Structured Noise with Self-Supervised Blind-Spot NetworksC. Broaddus, A. Krull, M. Weigert, U. Schmidt, and G. Myers. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), page 159--163. IEEE, (2020)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)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)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)

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CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

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CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting