Geert J. S. Litjens
Computer Vision and Pattern Recognition · 2013–2022
A researcher in the Science corpus: 18 papers; trusts 91 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 266 citations into it; #18,622 of 21,575 by trust here, #1,203 by citations
- Artificial Intelligence — 251 citations into it; #18,613 of 35,345 by trust here, #1,253 by citations
- Radiology, Nuclear Medicine and Imaging — 163 citations into it; #1,400 of 3,265 by trust here, #30 by citations
- Model Architectures — 58 citations into it
- Optimization & Training — 39 citations into it
- Medical Imaging — 33 citations into it
- Statistics and Probability — 12 citations into it
Whom Geert J. S. Litjens trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yann LeCun
- Diederik P. Kingma
- Kaiming He
- Ilya Sutskever
- J. Urgen Schmidhuber
- Dan Claudiu Ciresan
- Jerome H. Friedman
Who trusts Geert J. S. Litjens overall
- Mingxia Liu
- Md. Zahangir Alom
- Alireza Mehrtash
- Davood Karimi
- Yun Liu
- J. A. van der Laak
- Fabian Isensee
- Chetan L. Srinidhi
- Shijun Wang
- Juan Eugenio Iglesias
Papers from Geert J. S. Litjens's perspective
Ordered by how strongly the researchers Geert J. S. Litjens 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
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- 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
- Going deeper with convolutions
Their papers
- A survey on deep learning in medical image analysis (2017) — 13,846 citations
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer (2017) — 3,174 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
- The Medical Segmentation Decathlon (2021) — 1,513 citations
- A large annotated medical image dataset for the development and evaluation of segmentation algorithms (2019) — 1,013 citations
- Large scale deep learning for computer aided detection of mammographic lesions (2017) — 912 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
- Metrics Reloaded: Recommendations for image analysis validation (2022) — 479 citations
- From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge (2019) — 709 citations
- State-of-the-Art Deep Learning in Cardiovascular Image Analysis. (2019) — 352 citations
- Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. (2019) — 569 citations
- Using deep learning to segment breast and fibroglandular tissue in MRI volumes (2017) — 204 citations
- Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks (2016) — 1,170 citations
- Computer-Aided Detection of Prostate Cancer in MRI (2014) — 467 citations
- Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge (2013) — 882 citations