Accurate detection of severe aortic stenosis based on self-supervised and ensemble learning of single-view echocardiograms.
A large-scale benchmark for long-tailed learning of chest X-rays.
Self-supervised learning method for echocardiogram videos drives label-efficient fine-tuning on aortic stenosis classification. Presented at IMLH 2022, an ICML workshop.
A Transformer for weakly supervised disease localization that uses a novel feedback loop between (local) radiomics features and (global) chest X-ray features.
SPIE Medical Imaging 2022
Domain knowledge-guided method for improved rib fracture localization.
CVAMD 2021 (ICCV Workshop)
Methods for jointly learning from breast imaging and tabular non-image data to predict breast cancer.
SPIE Medical Imaging 2020
Segmentation methods for localizing "lines" (catheters, tubes, etc.) in pediatric chest radiographs.
SPIE Medical Imaging 2020 Oral
Automatic anatomic segmentation of pediatric chest radiographs under severe class imbalance.