Tucker J. Netherton

Tucker J. Netherton is an Assistant Professor at The University of Texas MD Anderson Cancer Center. His research focuses on radiation oncology, medical image computing, and treatment planning, with specific interests in automatic tumor segmentation, radiotherapy dose prediction, and the application of large language models to enhance medical software usability and failure mode analysis. His recent work includes developing the RADIANT framework for configurable dose prediction, uncertainty-guided pancreatic tumor auto-segmentation, and head and neck gross tumor volume segmentation using PocketNet. He has also investigated pre- and post-treatment glioma segmentation and the use of LLMs for heuristic usability assessment of medical software. His contributions span venues such as Lecture Notes in Computer Science, Advances in Radiation Oncology, and the International Journal of Radiation Oncology*Biology*Physics.