Paintings deteriorate over time due to aging and storage conditions, with cracks being a common form of degradation. Detecting and mapping these cracks is crucial for art analysis and restoration but it presents challenges. Traditional methods often …
This paper addresses a critical issue in seabed characterization with deep learning semantic segmentation using high-resolution Synthetic Aperture Sonar (SAS) data, that we call Catastrophic Receptive Field Overflow (CRFO). We propose novel methods, …
Functional connectivity expresses the correlation of brain activity between regions and helps in understanding and diagnosing neurological conditions and disorders. It also provides discriminative features for machine learning classifiers. We propose …
Deep subspace clustering is an effective method for clustering high-dimensional data, and it provides state-of-the-art results in clustering hyperspectral images(HSI). However, these methods typically suffer from the size of the so-called …
Despite their huge potential, deep learning-based models are still not trustful enough to warrant their adoption in clinical practice. The research on the interpretability and explainability of deep learning is currently attracting huge attention. …