5 citations · 37 across the 12 of their papers we have counts for
12 papers
Federated Conformal Predictors for Distributed Uncertainty Quantification
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy +2
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 ste…
Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction
Charles Lu, Syed Rakin Ahmed, Praveer Singh +1
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 dep…
Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets
Charles Lu, Anastasios N. Angelopoulos, Stuart Pomerantz
The regulatory approval and broad clinical deployment of medical AI have been hampered by the perception that deep learning models fail in unpredictable and possibly catastrophic w…
Three Applications of Conformal Prediction for Rating Breast Density in Mammography
Charles Lu, Ken Chang, Praveer Singh +1
Breast cancer is the most common cancers and early detection from mammography screening is crucial in improving patient outcomes. Assessing mammographic breast density is clinicall…
Distribution-Free Federated Learning with Conformal Predictions
Charles Lu, Jayasheree Kalpathy-Cramer
Federated learning has attracted considerable interest for collaborative machine learning in healthcare to leverage separate institutional datasets while maintaining patient privac…
Deploying clinical machine learning? Consider the following...
Charles Lu, Ken Chang, Praveer Singh +5
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 c…
Fair Conformal Predictors for Applications in Medical Imaging
Charles Lu, Andreanne Lemay, Ken Chang +2
Deep learning has the potential to automate many clinically useful tasks in medical imaging. However translation of deep learning into clinical practice has been hindered by issues…
Evaluating subgroup disparity using epistemic uncertainty in mammography
Charles Lu, Andreanne Lemay, Katharina Hoebel +1
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…
Addressing catastrophic forgetting for medical domain expansion
Sharut Gupta, Praveer Singh, Ken Chang +13
Model brittleness is a key concern when deploying deep learning models in real-world medical settings. A model that has high performance at one institution may suffer a significant…
"Name that manufacturer". Relating image acquisition bias with task complexity when training deep learning models: experiments on head CT
Giorgio Pietro Biondetti, Romane Gauriau, Christopher P. Bridge +2
As interest in applying machine learning techniques for medical images continues to grow at a rapid pace, models are starting to be developed and deployed for clinical applications…
An Overview and Case Study of the Clinical AI Model Development Life Cycle for Healthcare Systems
Charles Lu, Julia Strout, Romane Gauriau +4
Healthcare is one of the most promising areas for machine learning models to make a positive impact. However, successful adoption of AI-based systems in healthcare depends on engag…
Stacked Neural Networks for end-to-end ciliary motion analysis
Charles Lu, M. Marx, M. Zahid +3
Cilia are hairlike structures protruding from nearly every cell in the body. Diseases known as ciliopathies, where cilia function is disrupted, can result in a wide spectrum of dis…