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Found 13 papers, 3 papers with code
Data Acquisition via Experimental Design for Decentralized Data Markets
Acquiring high-quality training data is essential for current machine learning models.
Federated Conformal Predictors for Distributed Uncertainty Quantification
Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing step to already trained models.
Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction
Estimating the test performance of software AI-based medical devices under distribution shifts is crucial for evaluating the safety, efficiency, and usability prior to clinical deployment.
Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets
Our work applies these new uncertainty quantification methods -- specifically conformal prediction -- to a deep-learning model for grading the severity of spinal stenosis in lumbar spine MRI.
Three Applications of Conformal Prediction for Rating Breast Density in Mammography
Breast cancer is the most common cancers and early detection from mammography screening is crucial in improving patient outcomes.
Distribution-Free Federated Learning with Conformal Predictions
Federated learning has attracted considerable interest for collaborative machine learning in healthcare to leverage separate institutional datasets while maintaining patient privacy.
Deploying clinical machine learning? Consider the following...
Despite the intense attention and considerable investment into clinical machine learning research, relatively few applications have been deployed at a large-scale in a real-world clinical environment.
Fair Conformal Predictors for Applications in Medical Imaging
Deep learning has the potential to automate many clinically useful tasks in medical imaging.
Evaluating subgroup disparity using epistemic uncertainty in mammography
As machine learning (ML) continue to be integrated into healthcare systems that affect clinical decision making, new strategies will need to be incorporated in order to effectively detect and evaluate subgroup disparities to ensure accountability and generalizability in clinical workflows.
Addressing catastrophic forgetting for medical domain expansion
Notably, this approach degrades model performance at the original institution, a phenomenon known as catastrophic forgetting.
"Name that manufacturer". Relating image acquisition bias with task complexity when training deep learning models: experiments on head CT
Recognition of such bias is critical to develop robust, generalizable models that will be crucial for clinical applications in real-world data distributions.
An Overview and Case Study of the Clinical AI Model Development Life Cycle for Healthcare Systems
Healthcare is one of the most promising areas for machine learning models to make a positive impact.
Stacked Neural Networks for end-to-end ciliary motion analysis
We find that the combination of segmentation and classification networks in a single pipeline yields performance comparable to existing computational pipelines, while providing the additional benefit of an end-to-end, fully-automated analysis toolbox for ciliary motion.
