Summary of Snuffy: Efficient Whole Slide Image Classifier, by Hossein Jafarinia et al.
Snuffy: Efficient Whole Slide Image Classifier
by Hossein Jafarinia, Alireza Alipanah, Danial Hamdi, Saeed Razavi, Nahal Mirzaie, Mohammad Hossein Rohban
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Image and Video Processing (eess.IV)
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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 Snuffy architecture addresses the challenge of Whole Slide Image (WSI) classification with multiple instance learning (MIL) in digital pathology. By introducing a novel MIL-pooling method based on sparse transformers, Snuffy mitigates performance loss with limited pre-training and enables continual few-shot pre-training as a competitive option. The sparsity pattern is tailored for pathology and is theoretically proven to be a universal approximator with the tightest probabilistic sharp bound on the number of layers for sparse transformers. Experimental results show that Snuffy achieves superior WSI and patch-level accuracies on CAMELYON16 and TCGA Lung cancer datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to classify images from digital pathology using machine learning. Digital pathology is the study of cells and tissues under a microscope, and it’s very important for diagnosing diseases like cancer. The problem is that most current methods need a lot of training data and computational power. Snuffy is a new approach that uses a special kind of neural network to make better predictions with less training. It also works well even when the training data is not exactly the same as the data being tested. The results show that Snuffy performs better than other methods on two important datasets. |
Keywords
» Artificial intelligence » Classification » Few shot » Machine learning » Neural network
