no code implementations • 22 Jul 2024 • Dimitrios Kollias, Anastasios Arsenos, James Wingate, Stefanos Kollias
In particular, a novel framework, named SAM2CLIP2SAM, is introduced for segmentation that leverages the strengths of both Segment Anything Model (SAM) and Contrastive Language-Image Pre-Training (CLIP) to accurately segment the right and left lungs in CT scans, subsequently feeding these segmented outputs into RACNet for classification of COVID-19 and non-COVID-19 cases.
no code implementations • 1 Jun 2024 • Nikolaos Spanos, Anastasios Arsenos, Paraskevi-Antonia Theofilou, Paraskevi Tzouveli, Athanasios Voulodimos, Stefanos Kollias
Input-level augmentation can provide a solution to this problem by widening the domain space in the source dataset and boost performance on out-of-domain distributions.
no code implementations • 10 May 2024 • Vasileios Karampinis, Anastasios Arsenos, Orfeas Filippopoulos, Evangelos Petrongonas, Christos Skliros, Dimitrios Kollias, Stefanos Kollias, Athanasios Voulodimos
This paper presents a deep-learning framework that utilizes optical sensors for the detection, tracking, and distance estimation of non-cooperative aerial vehicles.
no code implementations • 10 May 2024 • Anastasios Arsenos, Vasileios Karampinis, Evangelos Petrongonas, Christos Skliros, Dimitrios Kollias, Stefanos Kollias, Athanasios Voulodimos
The main barrier to achieving fully autonomous flights lies in autonomous aircraft navigation.
no code implementations • 26 Apr 2024 • Emmanouil Seferis, Stefanos Kollias, Chih-Hong Cheng
Randomized smoothing (RS) has successfully been used to improve the robustness of predictions for deep neural networks (DNNs) by adding random noise to create multiple variations of an input, followed by deciding the consensus.
no code implementations • 12 Mar 2024 • Anastasios Arsenos, Dimitrios Kollias, Evangelos Petrongonas, Christos Skliros, Stefanos Kollias
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 confronted with various unfamiliar domains.
no code implementations • 10 Mar 2024 • Demetris Gerogiannis, Anastasios Arsenos, Dimitrios Kollias, Dimitris Nikitopoulos, Stefanos Kollias
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.
1 code implementation • 4 Mar 2024 • 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-AI-MIA)' Workshop of the 2024 Computer Vision and Pattern Recognition (CVPR) Conference.
1 code implementation • 8 Nov 2023 • Zacharias Anastasakis, Dimitrios Mallis, Markos Diomataris, George Alexandridis, Stefanos Kollias, Vassilis Pitsikalis
We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD).
no code implementations • 1 Mar 2023 • 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 deep neural networks in various applications, including medical imaging.
no code implementations • 9 Jun 2022 • 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 (ECCV 2022).
1 code implementation • 14 Oct 2021 • Anargyros Chatzitofis, Dimitrios Zarpalas, Stefanos Kollias, Petros Daras
DeepMoCap explores motion capture by automatically localizing and labeling reflectors on depth images and, subsequently, on 3D space.
1 code implementation • 14 Oct 2021 • Anargyros Chatzitofis, Leonidas Saroglou, Prodromos Boutis, Petros Drakoulis, Nikolaos Zioulis, Shishir Subramanyam, Bart Kevelham, Caecilia Charbonnier, Pablo Cesar, Dimitrios Zarpalas, Stefanos Kollias, Petros Daras
HUMAN4D is introduced to the computer vision and graphics research communities to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD and audio data cues.
no code implementations • 14 Jun 2021 • Dimitrios Kollias, Anastasios Arsenos, Levon Soukissian, Stefanos Kollias
In this paper we present the COV19-CT-DB database which is annotated for COVID-19, consisting of about 5, 000 3-D CT scans, We have split the database in training, validation and test datasets.
no code implementations • 1 May 2021 • 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 Intelligence (AI).
no code implementations • 7 Dec 2020 • Bashar Alhnaity, Stefanos Kollias, Georgios Leontidis, Shouyong Jiang, Bert Schamp, Simon Pearson
Finally, a recurrent neural network including LSTM and an attention mechanism is proposed for modelling long-term dependencies in the time series data.
no code implementations • NeurIPS 2020 • Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias
Rather than performing inefficient local iterative routing between adjacent capsule layers, we propose an alternative global view based on representing the inherent uncertainty in part-object assignment.
no code implementations • 1 Dec 2020 • 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 but not limited to exertion games, surveillance, sports analytics and healthcare.
no code implementations • 28 Jan 2020 • Mamatha Thota, Stefanos Kollias, Mark Swainson, Georgios Leontidis
The presence and accuracy of such information is critical to ensure a detailed understanding of the product and to reduce the potential for health risks.
no code implementations • 25 Nov 2019 • James Wingate, Ilianna Kollia, Luc Bidaut, Stefanos Kollias
The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging.
no code implementations • 1 Jul 2019 • Bashar Alhnaity, Simon Pearson, Georgios Leontidis, Stefanos Kollias
Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general.
1 code implementation • 27 May 2019 • 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.
Ranked #3 on
Image Classification
on smallNORB
no code implementations • 6 Mar 2019 • Shouyong Jiang, Marcus Kaiser, Shengxiang Yang, Stefanos Kollias, Natalio Krasnogor
It is demonstrated with empirical studies that the proposed test suite is more challenging to the dynamic multiobjective optimisation algorithms found in the literature.
no code implementations • 23 Jan 2019 • 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 convolutional, or convolutional-recurrent Neural Networks (DNNs).
1 code implementation • 26 Nov 2018 • Fabio De Sousa Ribeiro, Francesco Caliva, Mark Swainson, Kjartan Gudmundsson, Georgios Leontidis, Stefanos Kollias
Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks.
no code implementations • 26 Jul 2018 • Fabio De Sousa Ribeiro, Francesco Caliva, Dionysios Chionis, Abdelhamid Dokhane, Antonios Mylonakis, Christophe Demaziere, Georgios Leontidis, Stefanos Kollias
512 dimensional representations were extracted from the 3D-CNN and LSTM architectures, and used as input to a fused multi-sigmoid classification layer to recognise the perturbation type.