Aaron Y. Lee
Radiology, Nuclear Medicine and Imaging · 2016–2023
A researcher in the Science corpus: 7 papers; trusts 2 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Radiology, Nuclear Medicine and Imaging — 77 citations into it
- Artificial Intelligence — 62 citations into it; #34,558 of 35,345 by trust here, #27,551 by citations
Whom Aaron Y. Lee trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Aaron Y. Lee overall
Papers from Aaron Y. Lee's perspective
Ordered by how strongly the researchers Aaron Y. Lee trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet: A large-scale hierarchical image database
- A Simple Framework for Contrastive Learning of Visual Representations (International Conference on Machine Learning)
- Deep Neural Networks for Acoustic Modeling in Speech Recognition
- Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
- Big Self-Supervised Models are Strong Semi-Supervised Learners
- On Calibration of Modern Neural Networks
- On the Opportunities and Risks of Foundation Models
- Masked Autoencoders Are Scalable Vision Learners (Computer Vision and Pattern Recognition)
- Emerging Properties in Self-Supervised Vision Transformers (IEEE International Conference on Computer Vision)
- An Empirical Study of Training Self-Supervised Vision Transformers
Their papers
- Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension (2020) — 1,116 citations
- Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension (2020) — 1,148 citations
- A foundation model for generalizable disease detection from retinal images (2023) — 932 citations
- Applications of continual learning machine learning in clinical practice (2020) — 213 citations
- Artificial intelligence and deep learning in ophthalmology (2018) — 1,314 citations
- Deep-Learning Based, Automated Segmentation of Macular Edema in Optical Coherence Tomography (2017) — 315 citations
- Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration (2016) — 329 citations