A Convolutional Neural Network as a Proxy for the XRF Approximation of the Chemical Composition of Archaeological Artefacts in the Presence of Inter-microscope Variability. - Catalin Stoean; Leonard Ionescu; Ruxandra Stoean; Marinela Boicea; Miguel A. Atencia Ruiz; Gonzalo Joya (IWANN (2), 2021)

A Deep Learning-based Surrogate for the XRF Approximation of Elemental Composition within Archaeological Artefacts before Restoration. - Ruxandra Stoean; Leonard Ionescu; Catalin Stoean; Marinela Boicea; Miguel A. Atencia Ruiz; Gonzalo Joya (KES, 2021)

Semantic Segmentation for Corrosion Detection in Archaeological Artefacts before Restoration. - Ruxandra Stoean; Nebojsa Bacanin; Leonard Ionescu; Marinela Boicea; Alina-Maria Garau; Cristina-Camelia Ghitescu (SYNASC, 2021)

Deep learning for a swift non-invasive recognition and delineation of corrosive iron compounds present on the surface of unrestored archaeological artefacts. - Ruxandra Stoean; Nebojsa Bacanin; Leonard Ionescu; Catalin Stoean; Marinela Boicea; Alina-Maria Garau; Cristina-Camelia Ghitescu (KES, 2022)

On Using Perceptual Loss within the U-Net Architecture for the Semantic Inpainting of Textile Artefacts with Traditional Motifs. - Catalin Stoean; Nebojsa Bacanin; Ruxandra Stoean; Leonard Ionescu; Cristian Daniel Alecsa; Mircea Hotoleanu; Miguel A. Atencia Ruiz; Gonzalo Joya (SYNASC, 2022)

Study on Semantic Inpainting Deep Learning Models for Artefacts with Traditional Motifs. - Catalin Stoean; Nebojsa Bacanin; Zeev Volkovich; Leonard Ionescu; Ruxandra Stoean (IWANN (2), 2023)

Influence of Manual Inter-Observer Variability for the Performance of Deep Learning Models in Semantic Segmentation. - Catalin Stoean; Nebojsa Bacanin; Ruxandra Stoean; Leonard Ionescu; Alina-Maria Garau; Cristina-Camelia Ghitescu (SYNASC, 2023)