Yangming Ou

Computer Vision and Pattern Recognition · 2009–2015

A researcher in the Science corpus: 3 papers; trusts 2 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Yangming Ou trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Sarang C. Joshi
  2. Tom Kamiel Magda Vercauteren
  3. Daniel Rueckert
  4. Ben Glocker
  5. Yong Fan
  6. Karl Rohr
  7. Michael I. Miller
  8. Paul Aljabar
  9. Xavier Pennec
  10. Nikos Komodakis

Who trusts Yangming Ou overall

  1. Juan Eugenio Iglesias
  2. András P. Keszei
  3. Aristeidis Sotiras
  4. Sérgio Pereira
  5. Xiaohuan Cao
  6. Theodore D. Satterthwaite
  7. Mert Rory Sabuncu
  8. Christos Davatzikos
  9. Jean-Philippe Fortin
  10. Benjamin Berkels

Papers from Yangming Ou's perspective

Ordered by how strongly the researchers Yangming Ou trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation (IEEE Transactions on Medical Imaging)
  2. ImageNet classification with deep convolutional neural networks
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  6. Landmark matching via large deformation diffeomorphisms (IEEE Transactions on Image Processing)
  7. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  8. Diffeomorphic Registration Using B-Splines (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  9. New variants of a method of MRI scale standardization (IEEE Transactions on Medical Imaging)
  10. Image registration methods: a survey (Image and Vision Computing)

Their papers