Summary of Residual-based Language Models Are Free Boosters For Biomedical Imaging, by Zhixin Lai et al.
Residual-based Language Models are Free Boosters for Biomedical Imaging
by Zhixin Lai, Jing Wu, Suiyao Chen, Yucheng Zhou, Naira Hovakimyan
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 The proposed approach utilizes residual-based large language models (LLMs) as encoders for biomedical imaging tasks, a departure from traditional methods that rely on textual data. By extracting a frozen transformer block from pre-trained LLMs and using it to process visual tokens directly, the strategy achieves state-of-the-art results on MedMNIST-2D and 3D datasets. The approach demonstrates the efficacy of LLMs in boosting performance across various biomedical imaging applications, including 2D and 3D visual classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that large language models can be used to improve how well computers do certain jobs related to medical images. It does this by taking a part of the language model and using it to look at pictures directly, rather than needing words or text first. This helps machines get better at things like recognizing what’s in an image or telling if something is normal or abnormal. |
Keywords
* Artificial intelligence * Boosting * Classification * Language model * Transformer
