Pearse A. Keane
Radiology, Nuclear Medicine and Imaging · 2018–2021
A researcher in the Science corpus: 7 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Radiology, Nuclear Medicine and Imaging — 96 citations into it
- Ophthalmology — 68 citations into it
Whom Pearse A. Keane trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Pearse A. Keane overall
Papers from Pearse A. Keane's perspective
Ordered by how strongly the researchers Pearse A. Keane trusts endorse each paper. Same corpus, a different order for every researcher.
- Rethinking the Inception Architecture for Computer Vision
- Densely Connected Convolutional Networks
- Deep Neural Networks for Acoustic Modeling in Speech Recognition
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
- Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks (JAMA ophthalmology)
- States
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Clinically applicable deep learning for diagnosis and referral in retinal disease (2018) — 2,402 citations
- With an eye to AI and autonomous diagnosis (2018) — 246 citations
- The Lancet Global Health Commission on Global Eye Health: vision beyond 2020 (2021) — 1,255 citations
- A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. (2019) — 1,444 citations
- Deep learning in ophthalmology: The technical and clinical considerations. (2019) — 466 citations
- Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. (2019) — 251 citations
- Artificial intelligence and deep learning in ophthalmology (2018) — 1,314 citations