Geometric Understanding of Supervised and Unsupervised Deep Learning for Biomedical Image Reconstruction

Geometric Understanding of Supervised and Unsupervised Deep Learning for Biomedical Image Reconstruction

Institute for Pure & Applied Mathematics (IPAM) via YouTube Direct link

Two Approaches for CT Reconstruction

11 of 18

11 of 18

Two Approaches for CT Reconstruction

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Geometric Understanding of Supervised and Unsupervised Deep Learning for Biomedical Image Reconstruction

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  1. 1 Intro
  2. 2 Veep Learning for Image Reconstruction Diagnosis & analysis
  3. 3 Deep Learning Revolution for Inverse Problem
  4. 4 Classical Methods for Inverse Problems
  5. 5 Input Space Partitioning for Multiple Expressions
  6. 6 Lipschitz Continuity
  7. 7 Regularized Recon vs. Deep Recon
  8. 8 Ultrasound Acquisition Modes
  9. 9 Adaptive Beamformer
  10. 10 Image Domain Learning is Essential?
  11. 11 Two Approaches for CT Reconstruction
  12. 12 DBP Domain ROI Tomography
  13. 13 DBP Domain Conebeam Artifact Removal
  14. 14 Style Transfer : Power of Tight Frame U-net
  15. 15 Our Penalized LS Formulation
  16. 16 Unsupervised Blind Deconvolution Microscopy
  17. 17 Unsupervised Learning for Accelerated MRI
  18. 18 Summary

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