Tout DSpace

Parcourir par sujet "learning algorithm"

Voici les éléments 1-5 de 5

    • Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification 

      Iakovidis D.K., Georgakopoulos S.V., Vasilakakis M., Koulaouzidis A., Plagianakos V.P. (2018)
      This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only ...

    • DIANA-miRGen v4: Indexing promoters and regulators for more than 1500 microRNAs 

      Perdikopanis N., Georgakilas G.K., Grigoriadis D., Pierros V., Kavakiotis I., Alexiou P., Hatzigeorgiou A. (2021)
      Deregulation of microRNA (miRNA) expression plays a critical role in the transition from a physiological to a pathological state. The accurate miRNA promoter identification in multiple cell types is a fundamental endeavor ...

    • DTCo: An Ensemble SSL Algorithm for X-ray Classification 

      Livieris I., Kotsilieris T., Anagnostopoulos I., Tampakas V. (2020)
      In the last decades, the classification of images was established as a typical method for diagnosing many abnormalities and diseases. The purpose of an efficient classification method is considered essential in modern ...

    • Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review 

      Siouras A., Moustakidis S., Giannakidis A., Chalatsis G., Liampas I., Vlychou M., Hantes M., Tasoulis S., Tsaopoulos D. (2022)
      The improved treatment of knee injuries critically relies on having an accurate and costeffective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim ...

    • Video-Based Eye Blink Identification and Classification 

      Nousias G., Panagiotopoulou E.-K., Delibasis K., Chaliasou A.-M., Tzounakou A.-M., Labiris G. (2022)
      Blink detection and classification can provide a very useful clinical indicator, because of its relation with many neurological and ophthalmological conditions. In this work, we propose a system that automatically detects ...