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From the 1 of 37 linked papers with an AI index.

activity
20122021
most citedCheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

1.3k citations · 4k across the 21 of their papers we have counts for

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Showing cs.CVShow all

14 papers · 1 filter

cs.CV20214 cited

MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning

Akshay Smit, Damir Vrabac, Yujie He +3

We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources. Our method…

cs.CV2021

CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays

Emma Chen, Andy Kim, Rayan Krishnan +3

A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes. In this work,…

cs.CV2021

CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays

Siyu Shi, Ishaan Malhi, Kevin Tran +2

We systematically evaluate the performance of deep learning models in the presence of diseases not labeled for or present during training. First, we evaluate whether deep learning…

cs.CV202185 cited

CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation

Alexander Ke, William Ellsworth, Oishi Banerjee +2

Deep learning methods for chest X-ray interpretation typically rely on pretrained models developed for ImageNet. This paradigm assumes that better ImageNet architectures perform be…

cs.CV2021

CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation

Soham Gadgil, Mark Endo, Emily Wen +2

Medical image segmentation models are typically supervised by expert annotations at the pixel-level, which can be expensive to acquire. In this work, we propose a method that combi…

cs.CV202016 cited

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…