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Found 8 papers, 3 papers with code
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.
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
In this paper, we extend the 2D TransUNet architecture to a 3D network by building upon the state-of-the-art nnU-Net architecture, and fully exploring Transformers' potential in both the encoder and decoder design.
The Effect of Counterfactuals on Reading Chest X-rays
This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays.
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation.
Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging
A critical aspect of interaction design for AI-assisted human decision making are policies about the display and sequencing of AI inferences within larger decision-making workflows.
Active label cleaning for improved dataset quality under resource constraints
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have an often-overlooked confounding effect on the assessment of model performance.
CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays
In this study, we measured the diagnostic performance for 8 different chest x-ray models when applied to photos of chest x-rays.
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Labeling training datasets has become a key barrier to building medical machine learning models.
