Matthew P. Lungren
Artificial Intelligence · 2017–2024
A researcher in the Science corpus: 23 papers; trusts 447 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 311 citations into it; #22,194 of 35,345 by trust here, #7,531 by citations
- Computer Vision and Pattern Recognition — 232 citations into it
- Radiology, Nuclear Medicine and Imaging — 135 citations into it; #1,538 of 3,265 by trust here, #62 by citations
- Evaluation & Benchmarks — 64 citations into it
- Representation & Self-Supervised Learning — 58 citations into it
- Multimodal Learning — 53 citations into it
- Pulmonary and Respiratory Medicine — 52 citations into it; #260 of 770 by trust here, #29 by citations
- Natural Language Processing — 47 citations into it
Whom Matthew P. Lungren trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
- Ross B. Girshick
- Karen Simonyan
- Jeff Donahue
Who trusts Matthew P. Lungren overall
Papers from Matthew P. Lungren's perspective
Ordered by how strongly the researchers Matthew P. Lungren trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- 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
- MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports (2019) — 2,082 citations
- Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists (2018) — 1,270 citations
- AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining (2020) — 112 citations
- GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition (2021) — 567 citations
- CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT (2020) — 547 citations
- Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) — 810 citations
- A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. (2019) — 330 citations
- MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs (2017) — 89 citations
- Preparing Medical Imaging Data for Machine Learning (2020) — 891 citations
- TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers (2024) — 1,173 citations
- Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) — 468 citations
- The Current and Future State of AI Interpretation of Medical Images. (2023) — 337 citations
- BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs (2023) — 661 citations
- Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing (2023) — 304 citations
- AI recognition of patient race in medical imaging: a modelling study (2021) — 504 citations
- RadGraph: Extracting Clinical Entities and Relations from Radiology Reports (2021) — 435 citations
- MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs (2019) — 1,117 citations
- Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. (2019) — 203 citations