CAT: A Concept Annotation Tool for supporting the development of interpretable deep learning models

The deployment of interpretable deep learning models is usually constrained across several domains due to a fundamental scarcity of appropriately annotated data. Developing advanced interpretable models, particularly those designed for fine-grained classification, requires going beyond simple class labels to include rich metadata, such as attributes and part locations. This necessity is further amplified by growing international regulations that mandate the use of trustworthy and eXplainable Artificial Intelligence (XAI). Existing publicly available datasets often lack this crucial fine-grained information, necessitating custom annotation, often performed manually, for practical application. To overcome these challenges, we introduce the Concept Annotation Tool (CAT), a general-purpose, platform-independent annotation application. CAT is specifically designed to facilitate rapid and versatile enrichment of any image dataset. The application enables users to annotate each image by adding attributes, perform part annotation, and apply standard annotations, such as bounding boxes and image cropping. We demonstrate the practical utility and the general applicability of our tool by enriching two datasets, one from the agrifood sector related to mushroom classification, and another from healthcare making reference to pigmented skin lesion classification. Through these case studies, we demonstrate that using CAT significantly accelerates the assignment of fine-grained attributes for large datasets compared to manual methods.