Aydogan Ozcan

Biomedical Engineering · 2010–2020

A researcher in the Science corpus: 16 papers; trusts 36 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Biomedical Engineering — 94 citations into it; #10,463 of 10,734 by trust here, #3,650 by citations
• Computer Vision and Pattern Recognition — 81 citations into it; #19,953 of 21,575 by trust here, #10,500 by citations
• Atomic and Molecular Physics, and Optics — 45 citations into it; #5,196 of 7,135 by trust here, #1,658 by citations
• Artificial Intelligence — 43 citations into it; #34,010 of 35,345 by trust here, #11,921 by citations
• Electrical and Electronic Engineering — 43 citations into it; #15,254 of 16,539 by trust here, #5,574 by citations
• Media Technology — 38 citations into it
• Radiation — 30 citations into it; #315 of 407 by trust here, #110 by citations
• Model Architectures — 13 citations into it

Whom Aydogan Ozcan trusts overall

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

1. Joseph W. Goodman
2. David A. B. Miller
3. Yair Rivenson
4. Kaiming He
5. George Barbastathis
6. George M. Whitesides
7. Diederik P. Kingma
8. Geoffrey E. Hinton
9. Yann LeCun
10. Alex Krizhevsky

Who trusts Aydogan Ozcan overall

1. Peter Leonard McMahon
2. Yair Rivenson

Papers from Aydogan Ozcan's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Dropout: a simple way to prevent neural networks from overfitting
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• All-optical machine learning using diffractive deep neural networks (2018) — 2,293 citations
• Handheld high-throughput plasmonic biosensor using computational on-chip imaging (2014) — 353 citations
• Optical Imaging Techniques for Point-of-care Diagnostics (2012) — 343 citations
• Deep learning enables cross-modality super-resolution in fluorescence microscopy (2018) — 713 citations
• Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy (2012) — 518 citations
• Inference in artificial intelligence with deep optics and photonics (2020) — 901 citations
• Synthetic aperture-based on-chip microscopy (2015) — 233 citations
• Emerging Technologies for Next-Generation Point-of-Care Testing. (2015) — 613 citations
• Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning (2018) — 612 citations
• PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning (2018) — 388 citations
• Deep Learning Microscopy: Enhancing Resolution, Field-of-View and Depth-of-Field of Optical Microscopy Images Using Neural Networks (2017) — 403 citations
• Phase recovery and holographic image reconstruction using deep learning in neural networks (2017) — 955 citations
• Lensless Imaging and Sensing. (2016) — 317 citations
• Cellphone-Based Hand-Held Microplate Reader for Point-of-Care Testing of Enzyme-Linked Immunosorbent Assays. (2015) — 313 citations
• Mobile Phones Democratize and Cultivate Next-Generation Imaging, Diagnostics and Measurement Tools (2014) — 323 citations
• Lensfree microscopy on a cellphone (2010) — 476 citations