Author profile
Department of Radiology, University of California, San Francisco
To reduce reliance on labor-intensive voxel-wise tumor masks by training CT segmentation models directly from routine radiology and pathology reports, enabling scalable detection and localization of tumors relevant to radiotherapy planning and incidental find...
To provide a large, diverse, and quality-controlled abdominal CT dataset with pancreas- and tumor-centric voxel-wise annotations to support benchmarking and development of AI models for pancreatic target segmentation and anatomy-aware evaluation relevant to r...
To determine whether tumor descriptions in routine radiology reports can be converted into controllable priors for synthetic tumor generation in CT, and whether these report-conditioned synthetic tumors improve robustness of tumor detection and segmentation r...
Therapy Physics
To develop and validate an AI system that supports radiotherapy-relevant pancreatic target delineation by localizing and segmenting small pancreatic ductal adenocarcinoma (PDAC) and related anatomy on routine contrast-enhanced CT, and to benchmark performance...