Summary of Stain-invariant Representation For Tissue Classification in Histology Images, by Manahil Raza et al.
Stain-Invariant Representation for Tissue Classification in Histology Images
by Manahil Raza, Saad Bashir, Talha Qaiser, Nasir Rajpoot
First submitted to arxiv on: 21 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 The proposed framework generates stain-augmented versions of training images using stain matrix perturbation, which is then combined with a stain regularisation loss to enforce consistency between feature representations. This encourages the model to learn stain-invariant and domain-invariant feature representations, ultimately improving performance in cross-domain multi-class tissue type classification of colorectal cancer images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a way to create fake versions of training images by changing the staining process. They also added a special loss function that makes sure the model learns features that are not specific to certain stains or domains. This helps the model work better when classifying different types of tissue samples, especially when the staining process is different. |
Keywords
* Artificial intelligence * Classification * Loss function
