Extending Explainable Boosting Machines to Scientific Image Data
This work addresses the need for interpretable models in scientific domains like quantum technologies, but it is incremental as it extends an existing method to a new data type.
The authors tackled the problem of interpretability in computer vision for scientific applications by applying Explainable Boosting Machines (EBMs) to scientific image data, specifically cold-atom soliton images, and demonstrated that the approach provides explanations consistent with human intuition.
As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern. Driven by the pressing need for interpretable models in science, we propose the use of Explainable Boosting Machines (EBMs) for scientific image data. Inspired by an important application underpinning the development of quantum technologies, we apply EBMs to cold-atom soliton image data tabularized using Gabor Wavelet Transform-based techniques that preserve the spatial structure of the data. In doing so, we demonstrate the use of EBMs for image data for the first time and show that our approach provides explanations that are consistent with human intuition about the data.