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research_group:start [2020/05/11 19:41] abrodzicki |
research_group:start [2020/05/11 20:06] abrodzicki [Bacteria response clustering] |
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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. | 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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+ | The research topics include: | ||
+ | * fluorescence images | ||
+ | * image processing | ||
+ | * sharing information from different images | ||
+ | * convolitional neural networks | ||
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+ | Team members involved: | ||
+ | * J. Jaworek-Korjakowska | ||
+ | * A. Brodzicki | ||
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+ | ==== Bacteria response clustering ==== | ||
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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 topics include: | ||
+ | * data clustering | ||
+ | * bacteria response analysis | ||
+ | |||
+ | Team members involved: | ||
+ | * J. Jaworek-Korjakowska | ||
+ | * A. Brodzicki | ||