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20242026
most citedFine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncology

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT

Siqi Chen, Han Gong, Keyi Hou +3

Reliable organ localization in abdominal CT can provide spatial priors for downstream trauma analysis. We propose CT-3GDINO, a lightweight 3D detector that adapts a Grounding-DINO-…

cs.CV2026

Quantum CT via Dynamic Interval Encoding and Prior-Balanced QUBO Reconstruction

Ao Wang, Yikuang Yuluo, Yujie Liu +7

Quadratic unconstrained binary optimization (QUBO)-based quantum computed tomography (CT) casts reconstruction as a binary quadratic problem for quantum annealing and hybrid quantu…

cs.CV2025

Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRI

Ahmed Gomaa, Annette Schwarz, Ludwig Singer +23

Background: Differentiating radiation necrosis (RN) from tumor progression after stereotactic radiosurgery (SRS) remains a critical challenge in brain metastases. While histopathol…

eess.IV2025

A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastoma

Ahmed Gomaa, Yixing Huang, Pluvio Stephan +19

Accurate differentiation of pseudoprogression (PsP) from True Progression (TP) following radiotherapy (RT) in glioblastoma (GBM) patients is crucial for optimal treatment planning.…

cs.AI20242 cited

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…