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Stroke Lesion Outcome Prediction Based on 4D CT Perfusion Data Using Temporal Convolutional Networks.

K. Amador, M. Wilms, A. Winder, J. Fiehler, and N. Forkert. MIDL, volume 143 of Proceedings of Machine Learning Research, page 22-33. PMLR, (2021)

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Jens Fiehler

Katja Fiehler

Fritz Fiehler

Konrad Fiehler

Klaus Fiehler

Other publications of authors with the same name

Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks.A. Tuladhar, S. Schimert, D. Rajashekar, H. Kniep, J. Fiehler, and N. Forkert. IEEE Access, (2020)Brain network architecture constrains age-related cortical thinning.M. Petersen, F. Nägele, C. Mayer, M. Schell, D. Rimmele, E. Petersen, S. Kühn, J. Gallinat, U. Hanning, J. Fiehler and 4 other author(s). NeuroImage, (2022)Stroke Lesion Outcome Prediction Based on 4D CT Perfusion Data Using Temporal Convolutional Networks.K. Amador, M. Wilms, A. Winder, J. Fiehler, and N. Forkert. MIDL, volume 143 of Proceedings of Machine Learning Research, page 22-33. PMLR, (2021)Reversible, irreversible and effective transverse relaxation rates in normal aging brain at 3 T.J. Sedlacik, K. Boelmans, U. Löbel, B. Holst, S. Siemonsen, and J. Fiehler. NeuroImage, (2014)Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data.D. Kumar, H. Hariharan, T. Faizy, P. Borchert, S. Siemonsen, J. Fiehler, R. Reddy, and J. Sedlacik. NeuroImage, (2018)Better Diffusion Segmentation in Acute Ischemic Stroke Through Automatic Tree Learning Anomaly Segmentation.J. Boldsen, T. Engedal, S. Pedraza, T. Cho, G. Thomalla, N. Nighoghossian, J. Baron, J. Fiehler, L. Østergaard, and K. Mouridsen. Frontiers Neuroinformatics, (2018)Hybrid Spatio-Temporal Transformer Network for Predicting Ischemic Stroke Lesion Outcomes from 4D CT Perfusion Imaging.K. Amador, A. Winder, J. Fiehler, M. Wilms, and N. Forkert. MICCAI (3), volume 13433 of Lecture Notes in Computer Science, page 644-654. Springer, (2022)Machbarkeitsstudie zur CNN-basierten Identifikation und TICI-Klassifizierung zerebraler ischämischer Infarkte in DSA-Daten.M. Nielsen, M. Waldmann, A. Frölich, J. Fiehler, and R. Werner. Bildverarbeitung für die Medizin, page 200-205. Springer Vieweg, (2019)Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients.A. Gutierrez, A. Tuladhar, M. Wilms, D. Rajashekar, M. Hill, A. Demchuk, M. Goyal, J. Fiehler, and N. Forkert. Int. J. Comput. Assist. Radiol. Surg., 18 (5): 827-836 (May 2023)Negative Dip in BOLD fMRI Is Caused by Blood Flow - Oxygen Consumption Uncoupling In Humans.J. Röther, R. Knab, F. Hamzei, J. Fiehler, J. Reichenbach, C. Büchel, and C. Weiller. NeuroImage, 15 (1): 98-102 (2002)

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Disambiguation of "Fiehler, Jens"

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Stroke Lesion Outcome Prediction Based on 4D CT Perfusion Data Using Temporal Convolutional Networks.

Please choose a person to relate this publication to

Jens Fiehler

Katja Fiehler

Fritz Fiehler

Konrad Fiehler

Klaus Fiehler

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Fiehler, Jens"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Stroke Lesion Outcome Prediction Based on 4D CT Perfusion Data Using Temporal Convolutional Networks.

Please choose a person to relate this publication to

Jens Fiehler

Katja Fiehler

Fritz Fiehler

Konrad Fiehler

Klaus Fiehler

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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history of this post
URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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Stroke Lesion Outcome Prediction Based on 4D CT Perfusion Data Using Temporal Convolutional Networks.