Francesco Ciompi

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 7 papers; trusts 58 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 169 citations into it; #34,045 of 35,345 by trust here, #2,260 by citations
• Computer Vision and Pattern Recognition — 159 citations into it; #20,732 of 21,575 by trust here, #1,755 by citations
• Radiology, Nuclear Medicine and Imaging — 117 citations into it; #2,068 of 3,265 by trust here, #48 by citations
• Model Architectures — 33 citations into it
• Medical Imaging — 25 citations into it
• Optimization & Training — 19 citations into it

Whom Francesco Ciompi trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Diederik P. Kingma
6. Ilya Sutskever
7. Kaiming He
8. J. Urgen Schmidhuber
9. Karen Simonyan
10. Xavier Glorot

Who trusts Francesco Ciompi overall

1. B. van Ginneken
2. Geert J. S. Litjens
3. Muhammad Imran Razzak
4. Lequan Yu
5. Md. Zahangir Alom
6. Yin Dai

Papers from Francesco Ciompi's perspective

Ordered by how strongly the researchers Francesco Ciompi 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. ImageNet Large Scale Visual Recognition Challenge
7. Long Short-Term Memory
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• A survey on deep learning in medical image analysis (2017) — 13,846 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
• State-of-the-Art Deep Learning in Cardiovascular Image Analysis. (2019) — 352 citations
• Predicting breast tumor proliferation from whole‐slide images: The TUPAC16 challenge (2018) — 307 citations
• Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks (2016) — 1,170 citations
• Towards automatic pulmonary nodule management in lung cancer screening with deep learning (2016) — 392 citations