J. A. van der Laak
Artificial Intelligence · 2016–2022
A researcher in the Science corpus: 8 papers; trusts 49 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 174 citations into it; #22,467 of 35,345 by trust here, #1,827 by citations
- Computer Vision and Pattern Recognition — 152 citations into it; #19,343 of 21,575 by trust here, #1,444 by citations
- Radiology, Nuclear Medicine and Imaging — 108 citations into it; #3,012 of 3,265 by trust here, #59 by citations
- Model Architectures — 30 citations into it
- Medical Imaging — 25 citations into it
Whom J. A. van der Laak trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geert J. S. Litjens
- Maximilian Ilse
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Yann LeCun
- Alex Krizhevsky
- Dan Claudiu Ciresan
- Jakob Nikolas Kather
- Wouter Bulten
Who trusts J. A. van der Laak overall
Papers from J. A. van der Laak's perspective
Ordered by how strongly the researchers J. A. van der Laak trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- 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)
- Going deeper with convolutions
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
Their papers
- A survey on deep learning in medical image analysis (2017) — 13,846 citations
- Stain Specific Standardization of Whole-Slide Histopathological Images (2016) — 294 citations
- Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis (2016) — 1,025 citations
- Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology (2019) — 642 citations
- Deep learning in histopathology: the path to the clinic (2021) — 844 citations
- Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge (2022) — 592 citations
- From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge (2019) — 709 citations
- Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. (2019) — 569 citations