2.2k citations · 7.8k across the 48 of their papers we have counts for
16 papers · 1 filter
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…
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,…
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…
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…
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…
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…
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…
MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models
Hari Sowrirajan, Jingbo Yang, Andrew Y. Ng +1
Contrastive learning is a form of self-supervision that can leverage unlabeled data to produce pretrained models. While contrastive learning has demonstrated promising results on n…
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set
Damir Vrabac, Akshay Smit, Rebecca Rojansky +5
Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistentl…
Data augmentation with Mobius transformations
Sharon Zhou, Jiequan Zhang, Hang Jiang +2
Data augmentation has led to substantial improvements in the performance and generalization of deep models, and remain a highly adaptable method to evolving model architectures and…
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…
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
Pranav Rajpurkar, Awni Y. Hannun, Masoumeh Haghpanahi +2
We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single…
End-to-end people detection in crowded scenes
Russell Stewart, Mykhaylo Andriluka
Current people detectors operate either by scanning an image in a sliding window fashion or by classifying a discrete set of proposals. We propose a model that is based on decoding…
Deep learning for class-generic object detection
Brody Huval, Adam Coates, Andrew Ng
We investigate the use of deep neural networks for the novel task of class generic object detection. We show that neural networks originally designed for image recognition can be t…
Zero-Shot Learning Through Cross-Modal Transfer
Richard Socher, Milind Ganjoo, Hamsa Sridhar +3
This work introduces a model that can recognize objects in images even if no training data is available for the objects. The only necessary knowledge about the unseen categories co…