no code implementations • 20 Mar 2024 • Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael Jordan, Ramesh Raskar
Acquiring high-quality training data is essential for current machine learning models.
1 code implementation • 27 May 2023 • Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
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.
no code implementations • 12 Jul 2022 • Charles Lu, Syed Rakin Ahmed, Praveer Singh, Jayashree Kalpathy-Cramer
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.
1 code implementation • 5 Jul 2022 • Charles Lu, Anastasios N. Angelopoulos, Stuart Pomerantz
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.
no code implementations • 23 Jun 2022 • Charles Lu, Ken Chang, Praveer Singh, Jayashree Kalpathy-Cramer
Breast cancer is the most common cancers and early detection from mammography screening is crucial in improving patient outcomes.
no code implementations • 14 Oct 2021 • 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 privacy.
no code implementations • 14 Sep 2021 • Charles Lu, Ken Chang, Praveer Singh, Stuart Pomerantz, Sean Doyle, Sujay Kakarmath, Christopher Bridge, Jayashree Kalpathy-Cramer
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.
1 code implementation • 9 Sep 2021 • Charles Lu, Andreanne Lemay, Ken Chang, Katharina Hoebel, Jayashree Kalpathy-Cramer
Deep learning has the potential to automate many clinically useful tasks in medical imaging.
no code implementations • 6 Jul 2021 • Charles Lu, Andreanne Lemay, Katharina Hoebel, Jayashree Kalpathy-Cramer
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.
no code implementations • 24 Mar 2021 • Sharut Gupta, Praveer Singh, Ken Chang, Liangqiong Qu, Mehak Aggarwal, Nishanth Arun, Ashwin Vaswani, Shruti Raghavan, Vibha Agarwal, Mishka Gidwani, Katharina Hoebel, Jay Patel, Charles Lu, Christopher P. Bridge, Daniel L. Rubin, Jayashree Kalpathy-Cramer
Notably, this approach degrades model performance at the original institution, a phenomenon known as catastrophic forgetting.
no code implementations • 19 Aug 2020 • Giorgio Pietro Biondetti, Romane Gauriau, Christopher P. Bridge, Charles Lu, Katherine P. Andriole
Recognition of such bias is critical to develop robust, generalizable models that will be crucial for clinical applications in real-world data distributions.
no code implementations • 2 Mar 2020 • Charles Lu, Julia Strout, Romane Gauriau, Brad Wright, Fabiola Bezerra De Carvalho Marcruz, Varun Buch, Katherine Andriole
Healthcare is one of the most promising areas for machine learning models to make a positive impact.
no code implementations • 20 Mar 2018 • Charles Lu, M. Marx, M. Zahid, C. W. Lo, C. Chennubhotla, S. P. Quinn
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.