Lily H. Peng

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 18 papers; trusts 22 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 126 citations into it; #34,157 of 35,345 by trust here, #4,559 by citations
• Computer Vision and Pattern Recognition — 96 citations into it
• Model Architectures — 22 citations into it

Whom Lily H. Peng trusts overall

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

1. Geoffrey E. Hinton
2. Christian Szegedy
3. Yann LeCun
4. Alex Krizhevsky
5. Yoshua Bengio

Who trusts Lily H. Peng overall

1. Fei Wang
2. Maithra Raghu
3. Ursula Schmidt-Erfurth
4. Raquel Dias

Papers from Lily H. Peng's perspective

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

1. Gradient-based learning applied to document recognition
2. ImageNet: A large-scale hierarchical image database
3. Going deeper with convolutions
4. ImageNet classification with deep convolutional neural networks
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Rethinking the Inception Architecture for Computer Vision
9. Long Short-Term Memory
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Detecting Cancer Metastases on Gigapixel Pathology Images (2017) — 676 citations
• A deep learning system for differential diagnosis of skin diseases (2019) — 760 citations
• International evaluation of an AI system for breast cancer screening (2020) — 2,721 citations
• Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence (2021) — 676 citations
• Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge (2022) — 592 citations
• End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography (2019) — 1,861 citations
• Deep learning in ophthalmology: The technical and clinical considerations. (2019) — 466 citations
• Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy. (2019) — 379 citations
• How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature. (2019) — 474 citations
• Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program (2019) — 207 citations
• Similar image search for histopathology: SMILY (2019) — 154 citations
• Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer (2018) — 410 citations
• Artificial intelligence and deep learning in ophthalmology (2018) — 1,314 citations
• Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer (2018) — 458 citations
• Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists. (2018) — 318 citations
• Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy (2017) — 503 citations
• Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning (2017) — 1,503 citations
• Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. (2016) — 6,915 citations