Summary of Histokernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels For Pan-cancer Predictive Modelling, by Piotr Keller et al.
HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive Modelling
by Piotr Keller, Muhammad Dawood, Brinder Singh Chohan, Fayyaz ul Amir Afsar Minhas
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper proposes a new machine learning approach called HistoKernel to improve predictive models in computational pathology. The authors introduce a Maximum Mean Discrepancy (MMD) kernel, which explicitly characterizes distributional differences between patches within Whole Slide Images (WSIs). This allows for enhanced prediction performance on tasks such as survival and drug effect prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions about things like how long someone will live or what treatment will work best. To do this, they created a new way to measure how similar different parts of a very large image are. By comparing these images in a special way, they can make more accurate predictions. |
Keywords
* Artificial intelligence * Machine learning
