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Tarique Hussain

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Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations

Sep 18, 2022
Sven Koehler, Tarique Hussain, Hamza Hussain, Daniel Young, Samir Sarikouch, Thomas Pickhardt, Gerald Greil, Sandy Engelhardt

Figure 1 for Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations
Figure 2 for Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations
Figure 3 for Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations
Figure 4 for Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations
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Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks

Jan 20, 2021
Sven Koehler, Tarique Hussain, Zach Blair, Tyler Huffaker, Florian Ritzmann, Animesh Tandon, Thomas Pickardt, Samir Sarikouch, Heiner Latus, Gerald Greil, Ivo Wolf, Sandy Engelhardt

Figure 1 for Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks
Figure 2 for Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks
Figure 3 for Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks
Figure 4 for Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks
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Unsupervised Domain Adaptation from Axial toShort-Axis Multi-Slice Cardiac MR Images byIncorporating Pretrained Task Networks

Jan 19, 2021
Sven Koehler, Tarique Hussain, Zach Blair, Tyler Huffaker, Florian Ritzmann, Animesh Tandon, Thomas Pickardt, Samir Sarikouch, Heiner Latus, Gerald Greil, Ivo Wolf, Sandy Engelhardt

Figure 1 for Unsupervised Domain Adaptation from Axial toShort-Axis Multi-Slice Cardiac MR Images byIncorporating Pretrained Task Networks
Figure 2 for Unsupervised Domain Adaptation from Axial toShort-Axis Multi-Slice Cardiac MR Images byIncorporating Pretrained Task Networks
Figure 3 for Unsupervised Domain Adaptation from Axial toShort-Axis Multi-Slice Cardiac MR Images byIncorporating Pretrained Task Networks
Figure 4 for Unsupervised Domain Adaptation from Axial toShort-Axis Multi-Slice Cardiac MR Images byIncorporating Pretrained Task Networks
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How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?

Feb 10, 2020
Sven Koehler, Animesh Tandon, Tarique Hussain, Heiner Latus, Thomas Pickardt, Samir Sarikouch, Philipp Beerbaum, Gerald Greil, Sandy Engelhardt, Ivo Wolf

Figure 1 for How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?
Figure 2 for How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?
Figure 3 for How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?
Figure 4 for How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?
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