Dr Dimitrios Kollias
Lecturer in Artificial Intelligence
School of Electronic Engineering and Computer Science
Queen Mary University of London
Queen Mary University of London
Research
multimodal artificial intelligence, machine and deep learning, trustworthy (fair, responsible, explainable and ethical) artificial intelligence, medical imaging
Interests
I am developing and have an interest for creating novel multimodal artificial intelligence, machine and deep learning methods, as well as trustworthy (fair, responsible, explainable and ethical) models, for healthcare & precision medicine. I have worked with different pathologies (cancer, cardiovascular diseases, brain injuries, peridontal diseases, COVID-19 to name a few) and with a broad range of tissue and organs (intestine, liver, kidney, skin, bone, blood vessels and heart, blood-brain barrier, nerve to name a few).Publications
2024
Tao J, Ghosh S, Lian Z, Cai Z, Schuller BW, Dhall A, Zhao G, Kollias D, Cambria E, Goecke R and Gedeon T (2024). MRAC '24 Chairs' Welcome. MRAC 2024 - Proceedings of the 2nd International Workshop on Multimodal and Responsible Affective Computing Arsenos A, Petrongonas E, Filippopoulos O, Skliros C, Kollias D and Kollias S (2024). NEFELI: A deep-learning detection and tracking pipeline for enhancing autonomy in Advanced Air Mobility. Elsevier Aerospace Science and Technology 109613-109613. Miah H, Kollias D, Pedone GL, Provan D and Chen F (2024). Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A Feasibility Study. MDPI Diagnostics vol. 14, (13) Arsenos A, Karampinis V, Petrongonas E, Skliros C, Kollias D, Kollias S and Voulodimos A (2024). Common Corruptions for Enhancing and Evaluating Robustness in Air-to-Air Visual Object Detection. Institute of Electrical and Electronics Engineers IEEE Robotics and Automation Letters Morani K, Ayana EK, Kollias D and Unay D (2024). COVID‐19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier. Hindawi International Journal of Biomedical Imaging vol. 2024, (1) Morani K, Ayana EK, Kollias D and Unay D (2024). Detecting COVID-19 in Computed Tomography Images: A Novel Approach Utilizing Segmentation with UNet Architecture, Lung Extraction, and CNN Classifier. Intelligent Computing Springer Nature 2023
Kollias D, Vendal K, Gadhavi P and Russom S (2023). BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification. MDPI Applied Sciences vol. 13, (21) Kollias D, Arsenos A and Kollias S (2023). A deep neural architecture for harmonizing 3-D input data analysis and decision making in medical imaging. Elsevier Neurocomputing vol. 542, Chowdhury D, Das A, Dey A, Banerjee S, Golec M, Kollias D, Kumar M, Kaur G, Kaur R, Arya RC, Wander G, Wander P, Wander GS, Parlikad AK, Gill SS and Uhlig S (2023). CoviDetector: A transfer learning-based semi supervised approach to detect Covid-19 using CXR images. Elsevier BV BenchCouncil Transactions on Benchmarks, Standards and Evaluations vol. 3, (2) 100119-100119. Salpea N, Tzouveli P and Kollias D (2023). Medical Image Segmentation: A Review of Modern Architectures. Computer Vision – ECCV 2022 Workshops Springer Nature 2021
Yu M, Kollias D, Wingate J, Siriwardena N and Kollias S (2021). Machine Learning for Predictive Modelling of Ambulance Calls. MDPI Electronics vol. 10, (4) 2020
Kollias D and Zafeiriou S (2020). Exploiting Multi-CNN Features in CNN-RNN Based Dimensional Emotion Recognition on the OMG in-the-Wild Dataset. Institute of Electrical and Electronics Engineers (IEEE) IEEE Transactions on Affective Computing vol. 12, (3) 595-606. Kollias D, Cheng S, Ververas E, Kotsia I and Zafeiriou S (2020). Deep Neural Network Augmentation: Generating Faces for Affect Analysis. Springer Nature International Journal of Computer Vision vol. 128, (5) 1455-1484. 2019
Kollias D, Tzirakis P, Nicolaou MA, Papaioannou A, Zhao G, Schuller B, Kotsia I and Zafeiriou S (2019). Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond. Springer Nature International Journal of Computer Vision vol. 127, (6-7) 907-929. 2018
Tagaris A, Kollias D, Stafylopatis A, Tagaris G and Kollias S (2018). Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset. World Scientific Publishing International Journal of Artificial Intelligence Tools vol. 27, (03) 2017
Kollias D, Tagaris A, Stafylopatis A, Kollias S and Tagaris G (2017). Deep neural architectures for prediction in healthcare. Springer Nature Complex & Intelligent Systems vol. 4, (2) 119-131.