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Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes

M. Kloenne, S. Niehaus, L. Lampe, A. Merola, J. Reinelt, I. Roeder, and N. Scherf. Sci. Rep., 10 (1): 10712 (July 2020)

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Ontology patterns for the representation of quality changes of cells in timeP. Burek, N. Scherf, and H. Herre. J. Biomed. Semantics, 10 (1): 16 (October 2019)Automatic lung segmentation and quantification of aeration in computed tomography of the chest using 3D transfer learningL. Maiello, L. Ball, M. Micali, F. Iannuzzi, N. Scherf, R. Hoffmann, M. Gama de Abreu, P. Pelosi, and R. Huhle. Front. Physiol., (2021)Domain-specific cues improve robustness of deep learning-based segmentation of CT volumesM. Kloenne, S. Niehaus, L. Lampe, A. Merola, J. Reinelt, I. Roeder, and N. Scherf. Sci. Rep., 10 (1): 10712 (July 2020)Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer LearningL. Maiello, L. Ball, M. Micali, F. Iannuzzi, N. Scherf, R. Hoffmann, M. Gama de Abreu, P. Pelosi, and R. Huhle. Frontiers in Physiology, (February 2022)Forensic age estimation with Bayesian convolutional neural networks based on panoramic dental X-ray imagingW. de Back, S. Seurig, S. Wagner, B. Marré, I. Roeder, and N. Scherf. (2019)On the ontological foundations of cellular developmentP. Burek, N. Scherf, and H. Herre. (May 2020)linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browserJ. Waschke, M. Hlawitschka, K. Anlas, V. Trivedi, I. Roeder, J. Huisken, and N. Scherf. PLoS Comput. Biol., 17 (11): e1009503 (November 2021)Evaluating different k-space undersampling schemes with iterative and deep learning image reconstruction for fast multi-parameter mappingK. Podranski, K. Pine, T. Colnaghi, A. Marek, P. Scheibe, N. Scherf, and N. Weiskopf. (2022)Revealing the learning process in reinforcement learning agents through attention-oriented metricsC. Beylier, S. Hofmann, and N. Scherf. (2024)A pattern-based approach to a cell tracking ontologyP. Burek, N. Scherf, and H. Herre. Procedia Comput. Sci., (2019)

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Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes

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Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes