96 citations · 311 across the 20 of their papers we have counts for
24 papers
Uncertainty-guided Contrastive Learning for Single Source Domain Generalisation
Anastasios Arsenos, Dimitrios Kollias, Evangelos Petrongonas +2
In the context of single domain generalisation, the objective is for models that have been exclusively trained on data from a single domain to demonstrate strong performance when c…
COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System
Demetris Gerogiannis, Anastasios Arsenos, Dimitrios Kollias +2
Computer-aided diagnosis (CAD) systems stand out as potent aids for physicians in identifying the novel Coronavirus Disease 2019 (COVID-19) through medical imaging modalities. In t…
Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans
Dimitrios Kollias, Anastasios Arsenos, Stefanos Kollias
The paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the 'Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-…
Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction
Zacharias Anastasakis, Dimitrios Mallis, Markos Diomataris +3
We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD). Motivated by the effectiveness of Masked…
A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging
Dimitrios Kollias, Anastasios Arsenos, Stefanos Kollias
Harmonizing the analysis of data, especially of 3-D image volumes, consisting of different number of slices and annotated per volume, is a significant problem in training and using…
AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging
Dimitrios Kollias, Anastasios Arsenos, Stefanos Kollias
This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision…
HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media
Anargyros Chatzitofis, Leonidas Saroglou, Prodromos Boutis +9
We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric captu…
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors
Anargyros Chatzitofis, Dimitrios Zarpalas, Stefanos Kollias +1
In this paper, a marker-based, single-person optical motion capture method (DeepMoCap) is proposed using multiple spatio-temporally aligned infrared-depth sensors and retro-reflect…
MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis
Dimitrios Kollias, Anastasios Arsenos, Levon Soukissian +1
Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can assist medical specialists in vital circumstances. Deep learning methodologies constitute a main approach for…
AI-enabled Efficient and Safe Food Supply Chain
Ilianna Kollia, Jack Stevenson, Stefanos Kollias
This paper provides a review of an emerging field in the food processing sector, referring to efficient and safe food supply chains, from farm to fork, as enabled by Artificial Int…
An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth
Bashar Alhnaity, Stefanos Kollias, Georgios Leontidis +3
Multi-step prediction is considered of major significance for time series analysis in many real life problems. Existing methods mainly focus on one-step-ahead forecasting, since mu…
A compact sequence encoding scheme for online human activity recognition in HRI applications
Georgios Tsatiris, Kostas Karpouzis, Stefanos Kollias
Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including…
Deep Transparent Prediction through Latent Representation Analysis
D. Kollias, N. Bouas, Y. Vlaxos +6
The paper presents a novel deep learning approach, which extracts latent information from trained Deep Neural Networks (DNNs) and derives concise representations that are analyzed…
Multi-Source Deep Domain Adaptation for Quality Control in Retail Food Packaging
Mamatha Thota, Stefanos Kollias, Mark Swainson +1
Retail food packaging contains information which informs choice and can be vital to consumer health, including product name, ingredients list, nutritional information, allergens, p…
A Unified Deep Learning Approach for Prediction of Parkinson's Disease
James Wingate, Ilianna Kollia, Luc Bidaut +1
The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge e…
Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments
Bashar Alhnaity, Simon Pearson, Georgios Leontidis +1
Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general. Developing models which can effectively model growth and yie…
Capsule Routing via Variational Bayes
Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias
Capsule networks are a recently proposed type of neural network shown to outperform alternatives in challenging shape recognition tasks. In capsule networks, scalar neurons are rep…
A Scalable Test Suite for Continuous Dynamic Multiobjective Optimisation
Shouyong Jiang, Marcus Kaiser, Shengxiang Yang +2
Dynamic multiobjective optimisation has gained increasing attention in recent years. Test problems are of great importance in order to facilitate the development of advanced algori…
Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks
Ilianna Kollia, Andreas-Georgios Stafylopatis, Stefanos Kollias
This paper presents a new method for medical diagnosis of neurodegenerative diseases, such as Parkinson's, by extracting and using latent information from trained Deep convolutiona…
Deep Bayesian Self-Training
Fabio De Sousa Ribeiro, Francesco Caliva, Mark Swainson +3
Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks. However, with the ongoing uptake of such methods in industria…
Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis
Fabio De Sousa Ribeiro, Francesco Caliva, Dionysios Chionis +5
In this paper, we take the first steps towards a novel unified framework for the analysis of perturbations in both the Time and Frequency domains. The identification of type and so…
Combining guaranteed and spot markets in display advertising: Selling guaranteed page views with stochastic demand
Bowei Chen, Jingmin Huang, Yufei Huang +2
While page views are often sold instantly through real-time auctions when users visit websites, they can also be sold in advance via guaranteed contracts. In this paper, we present…
Global Vertices and the Noising Paradox
Konstantinos A. Raftopoulos, Stefanos D. Kollias, Marin Ferecatu
A theoretical and experimental analysis related to the identification of vertices of unknown shapes is presented. Shapes are seen as real functions of their closed boundary. Unlike…
Incremental Noising and its Fractal Behavior
Konstantinos A. Raftopoulos, Marin Ferecatu, Dionyssios D. Sourlas +1
This manuscript is about further elucidating the concept of noising. The concept of noising first appeared in \cite{CVPR14}, in the context of curvature estimation and vertex local…