Author profile
Johns Hopkins University
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 establish a large-scale, independent benchmark for evaluating auto-contouring AI with an emphasis on radiotherapy-relevant requirements: robustness to domain shift, calibration of confidence, and clinically meaningful failure modes beyond average Dice.
To provide a large, multicenter, longitudinal CT dataset with voxel-wise tumor annotations across multiple cancer sites to support development, benchmarking, and validation of AI models for radiotherapy target and organ-at-risk (OAR) segmentation under real-w...
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 create a large, quality-controlled abdominal CT atlas that enables radiotherapy auto-contouring research by providing standardized, voxel-wise annotations across diverse institutions and by supporting uncertainty-aware expert review and benchmarking.
To test whether a physics-informed “time-machine” tumor synthesis pipeline can generate realistic small pancreatic ductal adenocarcinoma (PDAC) targets on contrast-enhanced CT for training and stress-testing AI models intended to support CT-based target delin...
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...