Sharif Elguindi

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.

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.

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.