Sarah Hooper is a researcher in machine learning for medical imaging, affiliated with Stanford University (Electrical Engineering). Her work focuses on segmentation-for-classification, label-efficient training, and semi-supervised methods for medical image analysis.
Key Publications
- Reducing Reliance on Spurious Features in Medical Image Classification with Spatial Specificity (MLHC 2022)
- A Case for Reframing Automated Medical Image Classification as Segmentation (NeurIPS 2023)