Summary of Pathotune: Adapting Visual Foundation Model to Pathological Specialists, by Jiaxuan Lu et al.
PathoTune: Adapting Visual Foundation Model to Pathological Specialists
by Jiaxuan Lu, Fang Yan, Xiaofan Zhang, Yue Gao, Shaoting Zhang
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 framework, PathoTune, efficiently adapts visual foundation models to pathology-specific tasks by leveraging multi-modal prompts. The two domain gaps, Foundation-Task Gap and Task-Instance Gap, are identified as key challenges in adapting foundation models to downstream tasks. By using task-specific visual and textual prompts, along with instance-specific visual prompts, PathoTune outperforms single-modality prompt tuning approaches on multiple datasets at both patch-level and WSI-level. The framework also facilitates the direct adaptation of natural visual foundation models to pathological tasks, outperforming pathological foundation models with simple linear probing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PathoTune is a new way to use artificial intelligence to improve how well computers can understand images of diseased tissues. Right now, there are many different ways that AI is being used in this field, but most of them focus on training AI models using lots of images of diseased tissues. However, not all of these models are good at understanding the specific details of each image. The researchers behind PathoTune have developed a new way to use AI that is better at understanding individual images and can even use models that were trained on completely different types of images. This could be very helpful for doctors who need to look at lots of images to diagnose diseases. |
Keywords
* Artificial intelligence * Multi modal * Prompt
