Deborah Schmidt

Nature Methods · 2018–2021

A researcher in the Science corpus: 2 papers; trusts 0 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Papers from Deborah Schmidt's perspective

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

  1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  2. Gradient-based learning applied to document recognition
  3. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  4. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
  5. Deep Learning
  6. Generative Adversarial Nets (Neural Information Processing Systems)
  7. A non-local algorithm for image denoising
  8. Ilastik: Interactive learning and segmentation toolkit
  9. Cell Detection with Star-convex Polygons (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  10. Rapid three-dimensional isotropic imaging of living cells using Bessel beam plane illumination (Nature Methods)

Their papers