Summary of Efficient Whole Slide Image Classification Through Fisher Vector Representation, by Ravi Kant Gupta et al.
Efficient Whole Slide Image Classification through Fisher Vector Representation
by Ravi Kant Gupta, Dadi Dharani, Shambhavi Shanker, Amit Sethi
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 method introduces a novel approach to whole slide image (WSI) classification by automating the identification and examination of the most informative patches, reducing the need to process the entire slide. The two-stage approach involves extracting key patches based on their pathological significance and employing Fisher vectors (FVs) for representing features extracted from these patches. This method not only captures fine-grained details but also reduces computational overhead, making it efficient and scalable. The study evaluates the proposed method across multiple datasets, benchmarking its performance against comprehensive WSI analysis and contemporary weakly-supervised learning methodologies. The results show that the focused analysis of select patches, combined with Fisher vector representation, achieves comparable or even surpasses classification accuracy of standard practices, while significantly diminishing computational load and resource expenditure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study develops a new way to analyze whole slide images (WSIs) in digital pathology. Instead of looking at the entire image, it focuses on just a few key parts that are most important for diagnosing diseases. This method uses two steps: first, it picks out these important parts based on their significance; and second, it uses special mathematical tools called Fisher vectors to understand what makes them unique. This approach is more efficient and accurate than looking at the whole image, which can be very big and complicated. |
Keywords
» Artificial intelligence » Classification » Supervised
