Summary of From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer, by Zijiang Yang et al.
From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer
by Zijiang Yang, Zhongwei Qiu, Tiancheng Lin, Hanqing Chao, Wanxing Chang, Yelin Yang, Yunshuo Zhang, Wenpei Jiao, Yixuan Shen, Wenbin Liu, Dongmei Fu, Dakai Jin, Ke Yan, Le Lu, Hui Jiang, Yun Bian
First submitted to arxiv on: 21 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 The paper presents a large-scale dataset, WSI-Cell5B, containing over 5 billion cell-level annotations for whole slide images (WSIs). The dataset is based on 6,998 WSIs of 11 cancers from The Cancer Genome Atlas Program. A novel hierarchical Cell Cloud Transformer (CCFormer) model is also introduced to analyze the spatial distributions of cells in WSIs. CCFormer formulates the collection of cells as a cell cloud and models cell spatial distribution using Neighboring Information Embedding (NIE) and Hierarchical Spatial Perception (HSP). The model outperforms other methods in survival prediction and cancer staging, achieving state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a big dataset with lots of information about cells in special pictures. These pictures are very important for doctors to understand diseases like cancer. The researchers made a new way to look at these pictures by grouping the cells together and studying how they’re arranged. This helps them predict how likely someone is to survive or what stage their cancer is in. They found that this new method is really good at doing these things! |
Keywords
» Artificial intelligence » Embedding » Transformer
