Summary of Xmil: Insightful Explanations For Multiple Instance Learning in Histopathology, by Julius Hense et al.
xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology
by Julius Hense, Mina Jamshidi Idaji, Oliver Eberle, Thomas Schnake, Jonas Dippel, Laure Ciernik, Oliver Buchstab, Andreas Mock, Frederick Klauschen, Klaus-Robert Müller
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 a novel framework, xMIL, which refines the multiple instance learning (MIL) approach for weakly supervised machine learning. The authors demonstrate the effectiveness of their method in providing explanations for MIL models using layer-wise relevance propagation (LRP). They conduct extensive experiments on three toy settings and four real-world histopathology datasets, showcasing improved faithfulness scores compared to previous explanation attempts. The proposed framework enables pathologists to extract insights from MIL models, representing a significant advance for knowledge discovery and model debugging in digital histopathology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can make machine learning models more transparent and easier to interpret. The authors created a new way to explain why a computer says something is true or not true, especially when looking at pictures of cells. They tested this new method on lots of data and showed that it works better than other ways of explaining things. This is important because doctors need to understand how computers are helping them make decisions about patients. |
Keywords
» Artificial intelligence » Machine learning » Supervised
