Summary of Renal Cell Carcinoma Subtyping: Learning From Multi-resolution Localization, by Mohamad Mohamad et al.
Renal Cell Carcinoma subtyping: learning from multi-resolution localization
by Mohamad Mohamad, Francesco Ponzio, Santa Di Cataldo, Damien Ambrosetti, Xavier Descombes
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel self-supervised training strategy for machine learning diagnostic tools is proposed to reduce the need for annotated datasets in renal cell carcinoma diagnosis. The approach leverages the multi-resolution nature of histological samples to train models without sacrificing accuracy. The study demonstrates the effectiveness of this method on a whole slide imaging dataset for renal cancer subtyping, outperforming several state-of-the-art classification counterparts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Doctors are really good at diagnosing kidney cancer, but it can be hard to tell what type of cancer it is until it’s too late. This makes it harder to cure and increases the number of deaths from this type of cancer. To help doctors diagnose kidney cancer earlier and more accurately, researchers are using artificial intelligence (AI) to analyze images of the cancer. However, there isn’t enough data available for these AI tools to learn how to make accurate diagnoses. This study aims to find a way to train these AI tools without needing as much data. |
Keywords
* Artificial intelligence * Classification * Machine learning * Self supervised
