13 citations · 32 across the 7 of their papers we have counts for
9 papers
Exploring the Capabilities and Limitations of Large Language Models for Radiation Oncology Decision Support
Florian Putz, Marlen Haderleina, Sebastian Lettmaier +3
Thanks to the rapidly evolving integration of LLMs into decision-support tools, a significant transformation is happening across large-scale systems. Like other medical fields, the…
Task-Specific Data Preparation for Deep Learning to Reconstruct Structures of Interest from Severely Truncated CBCT Data
Yixing Huang, Fuxin Fan, Ahmed Gomaa +4
Cone-beam computed tomography (CBCT) is widely used in interventional surgeries and radiation oncology. Due to the limited size of flat-panel detectors, anatomical structures might…
Fine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncology
Yihao Hou, Christoph Bert, Ahmed Gomaa +12
Generating physician letters is a time-consuming task in daily clinical practice. This study investigates local fine-tuning of large language models (LLMs), specifically LLaMA mode…
Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation
Yixing Huang, Zahra Khodabakhshi, Ahmed Gomaa +7
Objectives: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of a…
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration
Yixing Huang, Christoph Bert, Ahmed Gomaa +3
Due to data privacy constraints, data sharing among multiple clinical centers is restricted, which impedes the development of high performance deep learning models from multicenter…
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding
Amr Hagag, Ahmed Gomaa, Dominik Kornek +5
Survival prediction for cancer patients is critical for optimal treatment selection and patient management. Current patient survival prediction methods typically extract survival i…