Nikos Tsiknakis

selected publications

1. Journal

   Deep Learning for Diabetic Retinopathy Detection and Classification Based on Fundus Images: A Review

   N. Tsiknakis, D. Theodoropoulos, G. Manikis, E. Ktistakis, O. Boutsora, A. Berto, F. Scarpa, A. Scarpa, D. Fotiadis, and K. Marias

   Computers in Biology and Medicine 2021

   Abs DOI Bib PDF

   Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models’ performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease’s lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions.

   @article{deeptsiknakis2021,
     abbr = {Journal},
     title = {Deep Learning for Diabetic Retinopathy Detection and Classification Based on Fundus Images: A Review},
     journal = {Computers in Biology and Medicine},
     publisher = {Elsevier},
     pages = {104599},
     year = {2021},
     issn = {0010-4825},
     pdf = {Tsiknakis - Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.pdf},
     doi = {https://doi.org/10.1016/j.compbiomed.2021.104599},
     simple_doi = {10.1016/j.compbiomed.2021.104599},
     url = {https://www.sciencedirect.com/science/article/pii/S0010482521003930},
     author = {Tsiknakis, Nikos and Theodoropoulos, Dimitris and Manikis, Georgios and Ktistakis, Emmanouil and Boutsora, Ourania and Berto, Alexa and Scarpa, Fabio and Scarpa, Alberto and Fotiadis, Dimitrios I. and Marias, Kostas},
     keywords = {artificial intelligence, classification, deep learning, detection, diabetic retinopathy, fundus, retina, review, segmentation},
     selected = {true},
     bibtex_show = {true}
   }

2. Journal

   Interpretable artificial intelligence framework for COVID‑19 screening on chest X‑rays

   N. Tsiknakis, E. Trivizakis, E. Vassalou, G. Papadakis, D. Spandidos, A. Tsatsakis, J. Sánchez‑García, R. López‑González, N. Papanikolaou, A. Karantanas, and K. Marias

   Experimental and Therapeutic Medicine 2020

   Abs DOI Bib PDF Code

   COVID-19 has led to an unprecedented healthcare crisis with millions of infected people across the globe often pushing infrastructures, healthcare workers and entire economies beyond their limits. The scarcity of testing kits, even in developed countries, has led to extensive research efforts towards alternative solutions with high sensitivity. Chest radiological imaging paired with artificial intelligence (AI) can offer significant advantages in diagnosis of novel coronavirus infected patients. To this end, transfer learning techniques are used for overcoming the limitations emanating from the lack of relevant big datasets, enabling specialized models to converge on limited data, as in the case of X‑rays of COVID‑19 patients. In this study, we present an interpretable AI framework assessed by expert radiologists on the basis on how well the attention maps focus on the diagnostically‑relevant image regions. The proposed transfer learning methodology achieves an overall area under the curve of 1 for a binary classification problem across a 5‑fold training/testing dataset.

   @article{tsiknakis2020covid,
     abbr = {Journal},
     author = {Tsiknakis, Nikos and Trivizakis, Eleftherios and Vassalou, Evangelia E. and Papadakis, Georgios Z. and Spandidos, Demetrios A. and Tsatsakis, Aristidis and Sánchez‑García, Jose and López‑González, Rafael and Papanikolaou, Nikolaos and Karantanas, Apostolos H. and Marias, Kostas},
     title = {Interpretable artificial intelligence framework for COVID‑19 screening on chest X‑rays},
     journal = {Experimental and Therapeutic Medicine},
     publisher = {Spandidos Publications},
     days = {27},
     month = may,
     year = {2020},
     doi = {https://doi.org/10.3892/etm.2020.8797},
     simple_doi = {10.3892/etm.2020.8797},
     code = {https://github.com/tsikup/covid19-xray-cnn},
     pdf = {Interpretable_artificial_intelligence_framework_for_COVID_19_screening_on_chest_X_rays.pdf},
     keywords = {COVID‐19, chest X‐rays, interpretable artificial intelligence, transfer learning},
     selected = {true},
     bibtex_show = {true}
   }