The Machine Vision Research Group (MVG Group) focuses on interdisciplinary research problems from the field of machine vision and medicine. The problems are solved using methods of digital image processing and analysis, machine learning and deep learning.
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Extending the knowledge of melanoma classification by exploring areas such as:
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To improve skin melanoma diagnosis by providing additional means of automatic detection of important diagnostic features in histopathological images.
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To detect anomalies in multivariate diagnostic signals of the synchrotron control system by pre-trained CNN architectures.
SOLARIS National Synchrotron Radiation Centre is a research facility that provides high quality synchrotron light. To control such a complex system it is necessary to monitor signals from various devices and subsystems. Anomaly detection prevents from financial loss, unplanned downtimes and in extreme cases cause damage. As artificial intelligence techniques including machine learning and deep neural networks have become state-of-the-art solutions for anomaly detection tasks which are one of the most challenging in data analysis, our team conducts research on the use of them for anomaly detection in multivariate diagnostic signals.
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To improve speed and quality of testing new drugs against Clostridium difficile infection, we developed an algorithm for automatic bacteria cytotoxicity classification. It was based on two kinds of fluorescence images - DAPI and GFP. We experimented with many different methods from classical image processing and machine learning algorithms to convolutional neural networks. This research was was conducted in cooperation with Stanford University.
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Newly opened project, in cooperation with Stanford University. The main idea is to analyse bacteria reaction in response to different serums.
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The research covers the topic of reinforcement learning used for robotic arm optimal trajectory planning.
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