1.3k citations · 1.7k across the 15 of their papers we have counts for
15 papers
Weakly-semi-supervised object detection in remotely sensed imagery
Ji Hun Wang, Jeremy Irvin, Beri Kohen Behar +4
Deep learning for detecting objects in remotely sensed imagery can enable new technologies for important applications including mitigating climate change. However, these models oft…
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste, Nils Lehmann, Pau Rodriguez +14
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to do…
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems
Cara Van Uden, Jeremy Irvin, Mars Huang +4
Self-supervised learning (SSL) enables label efficient training for machine learning models. This is essential for domains such as medical imaging, where labels are costly and time…
Improving debris flow evacuation alerts in Taiwan using machine learning
Yi-Lin Tsai, Jeremy Irvin, Suhas Chundi +3
Taiwan has the highest susceptibility to and fatalities from debris flows worldwide. The existing debris flow warning system in Taiwan, which uses a time-weighted measure of rainfa…
METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping
Bryan Zhu, Nicholas Lui, Jeremy Irvin +7
Reducing methane emissions is essential for mitigating global warming. To attribute methane emissions to their sources, a comprehensive dataset of methane source infrastructure is…
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Alexandre Lacoste, Evan David Sherwin, Hannah Kerner +9
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downst…
Structured dataset documentation: a datasheet for CheXpert
Christian Garbin, Pranav Rajpurkar, Jeremy Irvin +2
Billions of X-ray images are taken worldwide each year. Machine learning, and deep learning in particular, has shown potential to help radiologists triage and diagnose images. Howe…
OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery
Hao Sheng, Jeremy Irvin, Sasankh Munukutla +10
At least a quarter of the warming that the Earth is experiencing today is due to anthropogenic methane emissions. There are multiple satellites in orbit and planned for launch in t…
CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays
Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek +3
The use of smartphones to take photographs of chest x-rays represents an appealing solution for scaled deployment of deep learning models for chest x-ray interpretation. However, t…
ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery
Jeremy Irvin, Hao Sheng, Neel Ramachandran +7
Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies. In this work, we de…
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
Eric Zelikman, Sharon Zhou, Jeremy Irvin +7
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-…
CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting
Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek +5
Although there have been several recent advances in the application of deep learning algorithms to chest x-ray interpretation, we identify three major challenges for the translatio…
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko +17
Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that conta…
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu +9
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural networ…
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
Pranav Rajpurkar, Jeremy Irvin, Aarti Bagul +11
We introduce MURA, a large dataset of musculoskeletal radiographs containing 40,561 images from 14,863 studies, where each study is manually labeled by radiologists as either norma…