The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning
F Putz, S Beirami, MA Schmidt, MS May, J Grigo… - Strahlentherapie und …, 2024 - Springer
Background Promptable foundation auto-segmentation models like Segment Anything (SA,
Meta AI, New York, USA) represent a novel class of universal deep learning auto …
Meta AI, New York, USA) represent a novel class of universal deep learning auto …
Fine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncology
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 models …
investigates local fine-tuning of large language models (LLMs), specifically LLaMA models …
[HTML][HTML] Transfer Learning Approaches for Brain Metastases Screenings
MSK Luu, BN Tuchinov, V Suvorov, RM Kenzhin… - Biomedicines, 2024 - mdpi.com
Background: In this study, we examined the effectiveness of transfer learning in improving
automatic segmentation of brain metastases on magnetic resonance imaging scans, with …
automatic segmentation of brain metastases on magnetic resonance imaging scans, with …