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3DUnet

Yankai Liang

Created on January 29, 2024

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Transcript

PRESENTATION

3D U-Net

ALTAR DIT ALTER YVAN, LENTO ORSON, LIANG YANKAI

INDEX

1. Paper Information

2. Domain Information

3. Scientific / technical description

4. Results

5. Conclusion / Interests / Perspectives

6. References

Paper Information

TITLE

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

journal

International Conference on Medical Image Computing and Computer-Assisted Intervention

affiliations

authors

  1. Computer Science Department, University of Freiburg, Germany
  2. BIOSS Centre for Biological Signalling Studies, Freiburg, Germany
  3. University Hospital Freiburg, Renal Division, Faculty of Medicine, University of Freiburg, Germany
  4. Department of Psychiatry and Psychotherapy, University Medical Center Freiburg, Germany
  5. Google DeepMind, London, UK

Özgün Çiçek Ahmed Abdulkadir Soeren S. Lienkamp Thomas Brox Olaf Ronneberger

Domain Information

Context

Scientific/technical domain:

Scientific / technical description

Results

Conclusion / Interests / Perspectives

References

THANKS