Explaining AI Techniques such as SHAP LIME and RISE in Limited Sample Size Neuroimaging Studies

Abstract

A novel approach using powerful AI algorithms makes neuroscience research with limited sample sizes simpler to interpret and apply. The proposed prediction model uses neurological characteristics. Careful investigation of tiny inputs explains the model's projected effects. The approach uses Shapley values to properly evaluate a feature's usefulness, revealing neural features that impact predictions. Alternated examples also reveal how relevant local traits are, helping us understand their functions. The proposed technique outperforms SHAP, LIME, and RISE in readability, accuracy, resilience, computation efficiency, scalability, and model integrity. The proposed approach is the best way to interpret brain data, scoring 95 for simplicity of use and 90 for accuracy. It is also more reliable since it can generate simple visual assertions with consistent outcomes. This comprehensive strategy eliminates brain data interpretation issues and improves clinical and research decision-making. As MRI improves, the results support using sophisticated AI tools to study brain function and structure.

Ähnliche Arbeiten