Explainable AI (XAI) for medical applications with MATLAB
In recent years, artificial intelligence (AI) has shown great promise in medicine and medical device applications. However, strict interpretability and explainability regulatory requirements can make it prohibitively difficult to use AI-based algorithms for medical applications. To overcome these limitations, interpretable machine and deep learning techniques and algorithms have been developed to assess if a model behaves as expected or needs further development and training. In this talk, methods will be highlighted that help explain the predictions of deep neural networks applied to medical images such as MRI and X-Ray. You will learn about the interpretability methods readily available in MATLAB, such as occlusion sensitivity and gradient-weighted class activation mapping (Grad-CAM). We will illustrate how these methods can be applied in an interactive way using a MATLAB app-based [1] and command line workflows. Further, these methods will be put in the context of the complete AI workflow by showing an example where we develop an image segmentation network for cardiac MRI images using Grad-CAM [2].
[1] Explore Deep Network Explainability Using an App, GitHub.com. [2] Cardiac Left Ventricle Segmentation from Cine-MRI Images using U-Net, MATLAB Documentation.