NIH R01 · 2024
Guiding humans to create better labeled datasets for machine learning in biomedical research
PROJECT SUMMARY / ABSTRACT Machine learning (ML) has seen tremendous advances in the past decade, fueled by growth in computing and the availability of large labeled datasets. While the impact of these advances on clinical and biomedical research are potentially significant, these applications face unique challenges due to the difficulty in acquiring labels from biomedical experts. Furthermore, ML algorithms often fail to generalize across institutions or datasets due to measurement biases (e.g. MR scanners) or intrinsic demographic or biological differences between cohorts / datasets which limits their impact in biomedical science. This proposal will develop new methodology and open-source…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.