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08/23/2023
Augmenting medical image classifiers with synthetic data from latent diffusion models
While hundreds of artificial intelligence (AI) algorithms are now approv...
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08/09/2023
RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction
We present RadGraph2, a novel dataset for extracting information from ra...
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07/27/2023
Med-Flamingo: a Multimodal Medical Few-shot Learner
Medicine, by its nature, is a multifaceted domain that requires the synt...
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06/13/2023
Improving Zero-Shot Detection of Low Prevalence Chest Pathologies using Domain Pre-trained Language Models
Recent advances in zero-shot learning have enabled the use of paired ima...
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04/17/2023
BenchMD: A Benchmark for Modality-Agnostic Learning on Medical Images and Sensors
Medical data poses a daunting challenge for AI algorithms: it exists in ...
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04/02/2023
Video Pretraining Advances 3D Deep Learning on Chest CT Tasks
Pretraining on large natural image classification datasets such as Image...
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03/29/2023
Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation
Automated generation of clinically accurate radiology reports can improv...
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11/23/2022
Improving dermatology classifiers across populations using images generated by large diffusion models
Dermatological classification algorithms developed without sufficiently ...
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01/05/2022
Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia
In recent years, deep learning has successfully been applied to automate...
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08/03/2021
Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management
Recent advances in Natural Language Processing (NLP), and specifically a...
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06/28/2021
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
Extracting structured clinical information from free-text radiology repo...
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05/07/2021
Structured dataset documentation: a datasheet for CheXpert
Billions of X-ray images are taken worldwide each year. Machine learning...
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04/01/2021
Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation
Although deep learning models for chest X-ray interpretation are commonl...
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03/26/2021
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning
We propose a selective learning method using meta-learning and deep rein...
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03/18/2021
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays
A major obstacle to the integration of deep learning models for chest x-...
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03/08/2021
CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays
We systematically evaluate the performance of deep learning models in th...
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02/23/2021
VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels
Automatic extraction of medical conditions from free-text radiology repo...
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02/21/2021
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation
Self-supervised contrastive learning between pairs of multiple views of ...
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02/21/2021
CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation
Medical image segmentation models are typically supervised by expert ann...
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02/17/2021
CheXternal: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays and External Clinical Settings
Recent advances in training deep learning models have demonstrated the p...
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01/18/2021
CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation
Deep learning methods for chest X-ray interpretation typically rely on p...
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11/12/2020
CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays
The use of smartphones to take photographs of chest x-rays represents an...
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10/28/2020
GloFlow: Global Image Alignment for Creation of Whole Slide Images for Pathology from Video
The application of deep learning to pathology assumes the existence of d...
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10/11/2020
MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models
Self-supervised approaches such as Momentum Contrast (MoCo) can leverage...
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09/17/2020
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set
Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lym...
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07/13/2020
CheXphoto: 10,000+ Smartphone Photos and Synthetic Photographic Transformations of Chest X-rays for Benchmarking Deep Learning Robustness
Clinical deployment of deep learning algorithms for chest x-ray interpre...
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04/20/2020
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
The extraction of labels from radiology text reports enables large-scale...
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02/26/2020
CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting
Although there have been several recent advances in the application of d...
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01/21/2019
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Large, labeled datasets have driven deep learning methods to achieve exp...
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06/11/2018
Know What You Don't Know: Unanswerable Questions for SQuAD
Extractive reading comprehension systems can often locate the correct an...
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12/11/2017
MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs
We introduce MURA, a large dataset of musculoskeletal radiographs contai...
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11/25/2017
Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning
We build a deep reinforcement learning (RL) agent that can predict the l...
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11/14/2017
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
We develop an algorithm that can detect pneumonia from chest X-rays at a...
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07/06/2017
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
We develop an algorithm which exceeds the performance of board certified...
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06/16/2016
SQuAD: 100,000+ Questions for Machine Comprehension of Text
We present the Stanford Question Answering Dataset (SQuAD), a new readin...
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02/22/2016
Augur: Mining Human Behaviors from Fiction to Power Interactive Systems
From smart homes that prepare coffee when we wake, to phones that know n...
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12/07/2015
Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving
While emerging deep-learning systems have outclassed knowledge-based app...
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04/07/2015