This repository is a curated 'awesome list' of research resources on multimodal learning in medical imaging. It contains no application code, tests, or data pipelines; its sole substantive file is a large README.md that catalogs papers, datasets, benchmarks, and code repositories. Topics span medical vision-language models, report generation, visual question answering, retrieval-augmented generation (RAG), and multimodal LLMs. The list is organized by task and includes links to arXiv PDFs, GitHub code, and dataset sources, with badges indicating publication venues.
This repository is a curated collection of Jupyter notebooks and Python scripts that demonstrate fine-tuning, inference, quantization, and optimization techniques for state-of-the-art vision and multimodal models from the Hugging Face ecosystem. It covers a wide range of tasks including visual question answering, object detection, image classification, video understanding, multimodal retrieval, and preference alignment (DPO). The code is primarily educational, designed to be run interactively in Google Colab, with each notebook or script serving as a self-contained tutorial for a specific model or technique. Notable patterns include LoRA/QLoRA fine-tuning, knowledge distillation via a custom ImageDistilTrainer, any-to-any embedding for retrieval, and video frame downsampling for multimodal input. The repository is not a production library or application; it is a learning resource for engineers and researchers exploring vision-language models.