Tutorials

This tutorial introduces the fundamental concepts of large language models (LLMs) and their practical applications in the medical image analysis and reconstruction domain. It covers the following topics:

1. • Introduction to Large Language Models: Discussion of different LLM architectures and pre-training techniques, such as GPT-2 and GPT-3, and their applications in medical image analysis and reconstruction.
   • LLMs for Medical Image Analysis and Reconstruction: Practical applications of LLMs in processing and analyzing medical images to identify diseases and abnormalities.
   • Designing and Implementing LLM Projects: Guidelines for designing and implementing LLM projects for medical image classification, disease diagnosis, image reconstruction, or image synthesis.
   • Understanding LLM Architectures: Discussion of various LLM architectures and their strengths and weaknesses in the context of medical image analysis and reconstruction.
   • Critical Thinking and Problem-Solving Skills: Development of critical thinking and problem-solving skills in analyzing and interpreting medical images using LLMs and troubleshooting common problems in LLM projects.

By the end of this tutorial, participants will have acquired knowledge of LLMs and their practical applications in medical image analysis and reconstruction, understanding of pre-training and fine-tuning techniques for LLMs, ability to design and implement LLM projects, familiarity with various LLM architectures and their strengths and weaknesses in medical image analysis and reconstruction, and critical thinking and problem-solving skills in the context of medical image processing tasks.