The need for interpretable models has fostered the development of self-explainable
classifiers. Prior approaches are either based on multi-stage optimization schemes …

Deep Neural Networks (DNNs) excel at learning complex abstractions within their internal
representations. However, the concepts they learn remain opaque, a problem that becomes …

Deep learning-based approaches for content-based image retrieval (CBIR) of computed
tomography (CT) liver images is an active field of research, but suffer from some critical …

In recent months, the detection of COVID-19 from radiological images has become a topic of
significant interest. Several works have proposed different AI models to demonstrate the …

In the field of Computer Vision (CV), the degree to which two objects, eg two classes, share
a common conceptual meaning, known as semantic similarity, is closely linked to the visual …

Introduction This study aims to achieve early and reliable monitoring of wood-boring pests,
which are often highly concealed, have long lag times, and cause significant damage to …

The lack of interpretability and transparency in deep learning architectures has raised
concerns among professionals in various industries and academia. One of the main …

Recent developments in the field of explainable artificial intelligence (XAI) for vision models
investigate the information extracted by their feature encoder. We contribute to this effort and …

The majority of existing methods for machine learning-based medical image segmentation
are supervised models that require large amounts of fully annotated images. These types of …

With the ever-growing use of AI to solve real-world problems, the need for transparency and
trust in these methods has given rise to Interpretable and Explainable AI. While a growing …