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Auflistung nach Autor:in "Rexilius, Jan"

1 - 3 von 3
  • Konferenzbeitrag
    An application framework for rapid prototyping of clinically applicable software assistants
    (INFORMATIK 2006 – Informatik für Menschen, Band 1, 2006) Rexilius, Jan; Kuhnigk, Jan-Martin; Hahn, Horst K.; Peitgen, Heinz-Otto
    Computer assistance for clinical diagnosis support and treatment planning are continuously growing fields that have gained importance in several medical disciplines. Although a variety of algorithms are available today, only few are routinely applied for diagnosis support and treatment planning. We propose a hierarchical framework that allows for flexible and efficient development of clinically valuable software prototypes and for systematic evaluation of image processing methods in a research setting. A modular plug-in concept separates algorithmic and framework developments. Several basic components for data-, user-, and workflow-management provide a skeleton that can be customized by both application developer and user. Standardized interfaces allow the communication between both frame and application. Dedicated assistance is provided for an efficient radiological workflow integration. A flexible and simple handling of image processing and visualization algorithms within a modular programming interface is offered through an integration into a visual programming and rapid prototyping platform (MeVisLab). The capabilities of our framework are presented by means of exemplary prototypes, that are currently used in clinical practice.
  • Konferenzbeitrag
    NeuroQLab – A software assistant for neurosurgical planning and quantitative image analysis
    (Informatik 2009 – Im Focus das Leben, 2009) Weiler, Florian; Rexilius, Jan; Klein, Jan; Hahn, Horst K.
  • Konferenzbeitrag
    Pollen detection from honey sediments via Region-Based Convolutional Neural Networks
    (42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft, 2022) Viertel, Philipp; Koenig, Matthias; Rexilius, Jan
    This paper deals with the localization and classification of pollen grains in light-microscopic images from pollen samples and honey sediments. A laboratory analysis of the honey sediment offers precise information of the honey composition. By utilizing state of the art deep neural networks, we show the possibility of automatizing the process of pollen counting and identification. For that purpose, we created and labelled our own data set comprising two pollen classes and trained and evaluated a regional-based neural network. Our results show that the majority of pollen grains are correctly detected. The pollen frequency in the honey sediment is on par with the majority pollen class, however, more samples and further investigation are required to ensure stable results and practicality.