Aditya Apte

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 study examines how CT scan reconstruction kernels and tube current settings affect radiomic features, finding that some features are robust while others vary significantly.

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.