From Concept to Code: A General Framework for Building a Medical Vision-Language Baseline Model

Shuolin Yin

Keywords: mllm, vlm, medical vlm, Step-by-Step Guide

TL;DR: A practical guide for beginners building a medical Vision-Language Model. This tutorial covers project setup, multimodal data preparation, efficient QLoRA fine-tuning of Qwen-VL, and packaging the model with Docker for real-world inference.

Abstract: This tutorial provides a comprehensive, step-by-step framework for building a baseline medical Vision-Language Model (VLM), designed to bridge the gap between concept and code for newcomers in medical image computing. Using the FLARE 2025 challenge as a practical example, we deconstruct the model development process into four essential modules: Project Scoping & Setup, Data Handling, Model Fine-Tuning, and Inference & Deployment. Key concepts covered include selecting an appropriate open-source base model (e.g., Qwen-VL), creating a reproducible environment, implementing a robust data pipeline to handle diverse multimodal datasets, and applying memory-efficient fine-tuning techniques like QLoRA. The tutorial culminates in packaging the trained model for reproducible inference using Docker. This work aims to empower new researchers with the foundational skills and confidence to tackle complex medical AI challenges.

Submission Number: 4