Abstracting Deep Neural Networks into Concept Graphs for Concept Level Interpretability

Understand

The black-box nature of deep learning models prevents them from being completely trusted in domains like biomedicine.

• Most explainability techniques do not capture the concept-based reasoning that human beings follow.
• In this work, we attempt to understand the behavior of trained models that perform image processing tasks in the medical domain by building a graphical representation of the concepts they learn.
• Extracting such a graphical representation of the model's behavior on an abstract, higher conceptual level would unravel the learnings of these models and would help us to evaluate the steps taken by the model for predictions.