Search Results for author:
Found 25 papers, 16 papers with code
Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing
Our dataset (PMC-15M) is two orders of magnitude larger than existing biomedical image-text datasets such as MIMIC-CXR, and spans a diverse range of biomedical images.
Ranked #1 on
Medical Visual Question Answering
on SLAKE-English
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation
Given the prevalence of 3D medical imaging technologies such as MRI and CT that are widely used in diagnosing and treating diverse diseases, 3D segmentation is one of the fundamental tasks of medical image analysis.
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images.
Learning to Bootstrap for Combating Label Noise
Specifically, our method dynamically adjusts the per-sample importance weight between the real observed labels and pseudo-labels, where the weights are efficiently determined in a meta process.
Ranked #8 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.
RapidRead: Global Deployment of State-of-the-art Radiology AI for a Large Veterinary Teleradiology Practice
This work describes the development and real-world deployment of a deep learning-based AI system for evaluating canine and feline radiographs across a broad range of findings and abnormalities.
End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction
Deep learning techniques have emerged as a promising approach to highly accelerated MRI.
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data
Improving speed and image quality of Magnetic Resonance Imaging (MRI) via novel reconstruction approaches remains one of the highest impact applications for deep learning in medical imaging.
OncoPetNet: A Deep Learning based AI system for mitotic figure counting on H&E stained whole slide digital images in a large veterinary diagnostic lab setting
Approach: Multiple state-of-the-art deep learning techniques for histopathology image classification and mitotic figure detection were used in the development of OncoPetNet.
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14, 579 entities and 10, 889 relations), and a test dataset, which contains two independent sets of board-certified radiologist annotations for 100 radiology reports split equally across the MIMIC-CXR and CheXpert datasets.
High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning
Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis.
Structured dataset documentation: a datasheet for CheXpert
Following the structured format of Datasheets for Datasets, this paper expands on the original CheXpert paper and other sources to show the critical role played by radiologists in the creation of reliable labels and to describe the different aspects of the dataset composition in detail.
VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels
We also find that VisualCheXbert better agrees with radiologists labeling chest X-ray images than do radiologists labeling the corresponding radiology reports by an average F1 score across several medical conditions of between 0. 12 (95% CI 0. 09, 0. 15) and 0. 21 (95% CI 0. 18, 0. 24).
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays
We also found that the Latent Shift explanation allows a user to have more confidence in true positive predictions compared to traditional approaches (0. 15$\pm$0. 95 in a 5 point scale with p=0. 01) with only a small increase in false positive predictions (0. 04$\pm$1. 06 with p=0. 57).
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 potential to provide accurate chest X-ray interpretation and increase access to radiology expertise.
GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-Efficient Medical Image Recognition
In recent years, the growing number of medical imaging studies is placing an ever-increasing burden on radiologists.
CheXphoto: 10,000+ Photos and Transformations of Chest X-rays for Benchmarking Deep Learning Robustness
We introduce CheXphoto, a dataset of smartphone photos and synthetic photographic transformations of chest x-rays sampled from the CheXpert dataset.
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 training of medical imaging models.
Assessing Robustness to Noise: Low-Cost Head CT Triage
We then show that the trained model is robust to reduced tube current and fewer projections, with the AUROC dropping only 0. 65% for images acquired with a 16x reduction in tube current and 0. 22% for images acquired with 8x fewer projections.
CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting
First, we find that the top 10 chest x-ray models on the CheXpert competition achieve an average AUC of 0. 851 on the task of detecting TB on two public TB datasets without fine-tuning or including the TB labels in training data.
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies.
Ranked #93 on
Multi-Label Classification
on CheXpert
MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's thorax, but requiring specialized training for proper interpretation.
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries.
Ranked #1 on
Multi-Label Classification
on MRNet
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
To evaluate models robustly and to get an estimate of radiologist performance, we collect additional labels from six board-certified Stanford radiologists on the test set, consisting of 207 musculoskeletal studies.
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 level exceeding practicing radiologists.
Ranked #3 on
Pneumonia Detection
on ChestX-ray14
