Summary of Focused Active Learning For Histopathological Image Classification, by Arne Schmidt et al.
Focused Active Learning for Histopathological Image Classification
by Arne Schmidt, Pablo Morales-Álvarez, Lee A.D. Cooper, Lee A. Newberg, Andinet Enquobahrie, Aggelos K. Katsaggelos, Rafael Molina
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 Focused Active Learning (FocAL), a novel approach to efficient data acquisition in digital pathology using machine learning algorithms. By combining Bayesian Neural Networks with Out-of-Distribution detection, FocAL estimates different uncertainties for image selection, accounting for class imbalance, ambiguity, and artifacts. The method is tested on MNIST and the Panda dataset for prostate cancer classification, outperforming existing Active Learning methods by focusing on the most informative images and avoiding ambiguities and artifacts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists create a new way to get labeled data for machines that learn from pictures of cells. This helps doctors make better diagnoses by training computers to recognize patterns in cell images. The problem is that some images are tricky or don’t count because they’re not good quality. To fix this, the researchers developed FocAL, which uses special math to decide which images are most important and should be labeled first. They tested it on two different datasets and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Active learning » Classification » Machine learning
