Summary of Looking Deeper Into Interpretable Deep Learning in Neuroimaging: a Comprehensive Survey, by Md. Mahfuzur Rahman et al.
Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey
by Md. Mahfuzur Rahman, Vince D. Calhoun, Sergey M. Plis
First submitted to arxiv on: 14 Jul 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper reviews interpretable deep learning models in neuroimaging, discussing their current status, challenges, and limitations. It highlights the importance of model interpretability for applications in healthcare, finance, and law enforcement agencies. The authors summarize the progress of methods and opinions on interpretability resources, and discuss how recent studies leveraged model interpretability to understand brain disorders. The paper aims to advance scientific understanding by providing valuable insights and guidance for future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models are super smart because they can learn from raw data without needing extra help. This makes them really good at things like recognizing pictures or hearing voices. But sometimes it’s hard to figure out why the model made a certain decision, which is important in fields like healthcare and finance where safety matters. Explainable AI (XAI) helps us understand how models work by giving us clues about their thinking. Researchers are still trying to figure out what makes XAI methods reliable. This paper looks at how deep learning models can be made more understandable in neuroimaging, which is studying the brain. It talks about what’s currently happening, what’s not working well, and gives ideas for making things better. |
Keywords
* Artificial intelligence * Deep learning
