46 citations · 79 across the 5 of their papers we have counts for
9 papers
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
Joseph Boyd, Mykola Liashuha, Eric Deutsch +3
The examination of histopathology images is considered to be the gold standard for the diagnosis and stratification of cancer patients. A key challenge in the analysis of such imag…
Exploring Deep Registration Latent Spaces
Théo Estienne, Maria Vakalopoulou, Stergios Christodoulidis +8
Explainability of deep neural networks is one of the most challenging and interesting problems in the field. In this study, we investigate the topic focusing on the interpretabilit…
Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention
Mihir Sahasrabudhe, Stergios Christodoulidis, Roberto Salgado +5
Segmentation and accurate localization of nuclei in histopathological images is a very challenging problem, with most existing approaches adopting a supervised strategy. These meth…
AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia
Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella +28
Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia because of its availability and rapidity. The standard of referenc…
An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients
Ya Lu, Thomai Stathopoulou, Maria F. Vasiloglou +3
Regular monitoring of nutrient intake in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition. Although several methods to estimate nutr…
Self-Attention and Ingredient-Attention Based Model for Recipe Retrieval from Image Queries
Matthias Fontanellaz, Stergios Christodoulidis, Stavroula Mougiakakou
Direct computer vision based-nutrient content estimation is a demanding task, due to deformation and occlusions of ingredients, as well as high intra-class and low inter-class vari…