Curtis P. Langlotz
Artificial Intelligence · 2006–2023
A researcher in the Science corpus: 17 papers; trusts 457 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 240 citations into it; #14,776 of 35,345 by trust here, #6,422 by citations
- Computer Vision and Pattern Recognition — 155 citations into it; #14,334 of 21,575 by trust here, #1,890 by citations
- Radiology, Nuclear Medicine and Imaging — 107 citations into it
- Natural Language Processing — 81 citations into it
- Evaluation & Benchmarks — 60 citations into it
- Medical Imaging — 33 citations into it
- Multimodal Learning — 27 citations into it; #1,081 of 1,211 by trust here, #583 by citations
- Representation & Self-Supervised Learning — 27 citations into it; #3,074 of 3,145 by trust here, #1,415 by citations
Whom Curtis P. Langlotz trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Yoshua Bengio
- Ronald J. Williams
- Ilya Sutskever
- Yann LeCun
- Jimmy Ba
- Ross B. Girshick
Who trusts Curtis P. Langlotz overall
Papers from Curtis P. Langlotz's perspective
Ordered by how strongly the researchers Curtis P. Langlotz trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Contrastive Learning of Medical Visual Representations from Paired Images and Text (2020) — 1,105 citations
- CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison (2019) — 3,612 citations
- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning (2017) — 3,431 citations
- Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists (2018) — 1,270 citations
- Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports (2019) — 210 citations
- AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining (2020) — 112 citations
- Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation (2020) — 235 citations
- Learning to Summarize Radiology Findings (2018) — 139 citations
- RadLex: a new method for indexing online educational materials. (2006) — 465 citations
- A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. (2019) — 330 citations
- Deep Learning in Neuroradiology (2018) — 234 citations
- MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs (2017) — 89 citations
- FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare (2023) — 491 citations
- Video-based AI for beat-to-beat assessment of cardiac function (2020) — 1,040 citations
- Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. (2019) — 203 citations
- Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet (2018) — 702 citations
- MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs. (2017) — 410 citations