Anne L. Martel
Artificial Intelligence · 2017–2022
A researcher in the Science corpus: 5 papers; trusts 515 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 168 citations into it; #26,177 of 35,345 by trust here, #24,417 by citations
- Computer Vision and Pattern Recognition — 152 citations into it
- Radiology, Nuclear Medicine and Imaging — 75 citations into it; #1,955 of 3,265 by trust here, #2,127 by citations
- Biophysics — 34 citations into it
- Medical Imaging — 19 citations into it
- Representation & Self-Supervised Learning — 19 citations into it
- Model Architectures — 16 citations into it
- Safety, Robustness & Interpretability — 14 citations into it
Whom Anne L. Martel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Diederik P. Kingma
- Ross B. Girshick
- Alex Krizhevsky
- Yann LeCun
- Yoshua Bengio
- Karen Simonyan
- Ilya Sutskever
- Andrew Zisserman
Who trusts Anne L. Martel overall
Papers from Anne L. Martel's perspective
Ordered by how strongly the researchers Anne L. Martel trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Self supervised contrastive learning for digital histopathology (2020) — 412 citations
- Metrics Reloaded: Recommendations for image analysis validation (2022) — 479 citations
- Loss odyssey in medical image segmentation (2021) — 592 citations
- Deep neural network models for computational histopathology: A survey (2019) — 725 citations
- Using quantitative features extracted from T2-weighted MRI to improve breast MRI computer-aided diagnosis (CAD) (2017) — 19 citations