Summary of Serpent-vlm : Self-refining Radiology Report Generation Using Vision Language Models, by Manav Nitin Kapadnis et al.
SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision Language Models
by Manav Nitin Kapadnis, Sohan Patnaik, Abhilash Nandy, Sourjyadip Ray, Pawan Goyal, Debdoot Sheet
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel approach to generating accurate and coherent radiological reports is proposed by Radiology Report Generation (R2Gen), utilizing Multi-modal Large Language Models (MLLMs). The existing methods often hallucinate details, which can be mitigated by introducing a self-refining mechanism into the MLLM framework. SERPENT-VLM integrates a unique self-supervised loss that leverages similarity between pooled image representations and contextual representations of generated radiological text, alongside the standard Causal Language Modeling objective, to refine image-text representations. This approach reduces hallucination and enhances nuanced report generation. The model outperforms existing baselines like LLaVA-Med, BiomedGPT, etc., achieving State-of-the-Art (SoTA) performance on IU X-ray and Radiology Objects in COntext (ROCO) datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Radiology Report Generation uses big language models to make accurate and clear reports about what doctors see in medical images. Usually, these models just make things up that aren’t really there. But this new approach, called SERPENT-VLM, makes the model look at the image again and check if it’s making sense. This helps the model be more careful and make better reports. It even works when the image is a little messy. The model did much better than other models on two big tests. |
Keywords
» Artificial intelligence » Hallucination » Multi modal » Self supervised
