Summary of Towards a Multimodal Large Language Model with Pixel-level Insight For Biomedicine, by Xiaoshuang Huang et al.
Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine
by Xiaoshuang Huang, Lingdong Shen, Jia Liu, Fangxin Shang, Hongxiang Li, Haifeng Huang, Yehui Yang
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces MedPLIB, a novel multimodal large language model for the biomedical domain that supports pixel-level understanding and various modes of interaction. Building on recent advancements in Multimodal Large Language Models (MLLM), MedPLIB enables visual question answering (VQA), arbitrary pixel-level prompts, and pixel-level grounding. The authors propose a unique Mixture-of-Experts (MoE) multi-stage training strategy to coordinate multitask learning while maintaining computational efficiency. To advance biomedical MLLM research, the paper presents the Medical Complex Vision Question Answering Dataset (MeCoVQA), featuring 8 modalities for complex medical imaging question answering and image region understanding. Experimental results demonstrate MedPLIB’s state-of-the-art performance across various medical visual language tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a special kind of computer program that can understand and work with pictures and words from the medical field. The new program, called MedPLIB, can answer questions about what it sees in images and even respond to specific points or shapes within those images. To make this program work, the researchers developed a unique way of training it using lots of examples and practice. They also created a special dataset with many different types of medical images that they used to test the program’s abilities. The results show that MedPLIB is very good at doing these tasks and can even do them without being specifically trained for those tasks beforehand. |
Keywords
» Artificial intelligence » Grounding » Large language model » Mixture of experts » Question answering
