Summary of Benchmarking Domain Generalization Algorithms in Computational Pathology, by Neda Zamanitajeddin et al.
Benchmarking Domain Generalization Algorithms in Computational Pathology
by Neda Zamanitajeddin, Mostafa Jahanifar, Kesi Xu, Fouzia Siraj, Nasir Rajpoot
First submitted to arxiv on: 25 Sep 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 This paper evaluates 30 domain generalization (DG) algorithms on three computational pathology (CPath) tasks, using 7,560 cross-validation runs. The goal is to benchmark the effectiveness of these algorithms in the CPath context. The evaluation platform incorporates modality-specific techniques and recent advances like pretrained foundation models. The study finds that self-supervised learning and stain augmentation consistently outperform other methods, highlighting the potential of pretrained models and data augmentation. Additionally, a new pan-cancer tumor detection dataset (HISTOPANTUM) is introduced as a benchmark for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares 30 different ways to make deep learning models work better when applying them to unseen data in computational pathology tasks. The researchers did lots of tests with these methods on three different tasks and used special tools to help the algorithms perform well. They found that using self-supervised learning and stain augmentation worked really well, which is good news for people working on developing new medical imaging techniques. |
Keywords
» Artificial intelligence » Data augmentation » Deep learning » Domain generalization » Self supervised
