Summary of Attri-net: a Globally and Locally Inherently Interpretable Model For Multi-label Classification Using Class-specific Counterfactuals, by Susu Sun et al.
Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals
by Susu Sun, Stefano Woerner, Andreas Maier, Lisa M. Koch, Christian F. Baumgartner
First submitted to arxiv on: 8 Jun 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 paper introduces Attri-Net, an inherently interpretable neural network for multi-label classification that provides both local and global explanations. Unlike post-hoc explanation methods, which often suffer from conceptual problems, Attri-Net offers a more comprehensive understanding of its predictions. The model first generates class-specific attribution maps to highlight relevant disease evidence, then uses logistic regression classifiers based on these maps to make predictions. Local explanations are obtained by analyzing the weighted attribution maps, while global explanations are derived from the learned average representations of the attribution maps (class centers) and the linear classifier weights. To ensure the model’s explanations align with human knowledge, a mechanism is introduced to guide the model’s outputs. The proposed approach demonstrates high-quality explanations consistent with clinical knowledge without compromising classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes Attri-Net, a new way to understand how neural networks make decisions. Right now, most neural networks are like black boxes – we don’t know why they make certain predictions. This is a big problem when using these models in medical applications where accuracy and understanding are crucial. The researchers developed a new approach that helps us understand the decisions made by Attri-Net. They did this by creating maps that show what features of the data are most important for making a prediction. These maps can be used to explain individual predictions, as well as how the model works as a whole. The team also introduced a way to make sure the model’s explanations align with what we know about medicine. Overall, the paper shows that Attri-Net is able to provide high-quality explanations of its decisions without sacrificing accuracy. |
Keywords
» Artificial intelligence » Classification » Logistic regression » Neural network
