Maciej A. Mazurowski

Computer Vision and Pattern Recognition · 2008–2023

A researcher in the Science corpus: 7 papers; trusts 490 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 130 citations into it; #5,529 of 21,575 by trust here, #5,287 by citations
• Artificial Intelligence — 109 citations into it; #7,796 of 35,345 by trust here, #6,064 by citations
• Radiology, Nuclear Medicine and Imaging — 48 citations into it; #2,241 of 3,265 by trust here, #2,073 by citations
• Model Architectures — 30 citations into it
• Neurology — 24 citations into it; #465 of 1,455 by trust here, #773 by citations
• Biomedical Engineering — 19 citations into it
• Optimization & Training — 14 citations into it; #899 of 2,601 by trust here, #836 by citations
• Media Technology — 10 citations into it

Whom Maciej A. Mazurowski trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Yann LeCun
7. Ross B. Girshick
8. Shaoqing Ren
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Maciej A. Mazurowski overall

1. Mateusz Buda
2. Jun Ma
3. Eden Belouadah
4. Aida Ali
5. Arvid Lundervold
6. Shiori Sagawa
7. Hojjat Salehinejad
8. Ananya Kumar
9. Yuzhe Yang
10. Damien Teney

Papers from Maciej A. Mazurowski's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• A systematic study of the class imbalance problem in convolutional neural networks (2017) — 3,026 citations
• Segment Anything Model for medical image analysis: an experimental study (2023) — 813 citations
• Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm (2019) — 339 citations
• Deep learning for segmentation of brain tumors: Impact of cross‐institutional training and testing (2018) — 230 citations
• A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features (2018) — 301 citations
• Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018) — 523 citations
• Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance (2008) — 813 citations