Detection of Objects in Cryo-Electron Micrographs Using Geometric Deep Learning

Detection of Objects in Cryo-Electron Micrographs Using Geometric Deep Learning

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

Single particle cryoEM, grossly oversimplified

3 of 14

3 of 14

Single particle cryoEM, grossly oversimplified

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Detection of Objects in Cryo-Electron Micrographs Using Geometric Deep Learning

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  1. 1 Intro
  2. 2 Object detection, semantic segmentation, and instance segmentation
  3. 3 Single particle cryoEM, grossly oversimplified
  4. 4 In natural images, objects often have unknown orientations
  5. 5 Spatial decoders are image generative models that are equivariant to any coordinate transformation
  6. 6 Prior work: spatial-VAE combined the spatial decoder with an approximate inference network to learn disentangled object representations
  7. 7 Spatial-VAE fails to predict uniformly distributed rotations
  8. 8 Convolutional neural networks are translation equivariant but not rotation equivariant
  9. 9 Experiment setup and evaluation
  10. 10 TARGET-VAE learns invariant object representations, improving semantic clustering
  11. 11 Dimensionless Instance Segmentation Transformer (DIST)
  12. 12 3D instance segmentation of complex MT networks is challenging
  13. 13 Combining DIST with an upstream semantic segmentation network enables end-to-end tomogram analysis (TARDIS)
  14. 14 Using TARDIS for fully automated semantic and instance segmentation of microtubules in situ

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