Harini Veeraraghavan

This paper introduces a cloud-based framework for AI medical image analysis using pyCERR and Cancer Genomics Cloud, enabling end-to-end workflows without local software installation.

STRATIS provides cloud-enabled software workflows for AI modeling in radiotherapy and imaging analysis, supporting multi-institutional collaboration and deployment.

This paper develops deep learning models to automate segmentation of swallowing and chewing structures in H&N CT scans, achieving high accuracy and clinical utility.

This paper presents a framework for deploying deep learning segmentation models for medical images across operating systems and languages, enabling clinical use and multi-institutional studies.

The paper presents an open-source library of deep-learning models for image segmentation and outcomes in oncology, enabling validation, automation, and integration into clinical workflows.

This study develops CT-based texture analysis to quantify intra- and inter-site tumor heterogeneity in HGSOC and shows it correlates with progression-free survival.