Summary of Wsi-vqa: Interpreting Whole Slide Images by Generative Visual Question Answering, By Pingyi Chen et al.
WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering
by Pingyi Chen, Chenglu Zhu, Sunyi Zheng, Honglin Li, Lin Yang
First submitted to arxiv on: 8 Jul 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 The proposed WSI-VQA framework for interpreting whole slide images (WSIs) aims to revolutionize the workflow of pathological reading by reframing various tasks in a question-answering pattern. This allows pathologists to achieve immunohistochemical grading, survival prediction, and tumor subtyping through human-machine interaction. The framework consists of a generative visual question answering model named Wsi2Text Transformer (W2T), which outperforms existing discriminative models in medical correctness. Additionally, the framework establishes a dataset containing 8672 slide-level question-answering pairs with 977 WSIs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a superpower that lets you quickly and accurately diagnose diseases by looking at pictures of cells. That’s basically what this new technology can do! It uses a special kind of AI called generative visual question answering to help doctors understand what they’re seeing in these cell images. This means they can make more accurate diagnoses faster, which is really important for people who are sick. |
Keywords
» Artificial intelligence » Question answering » Transformer
