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MVG Research Group
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.
Research area
Deep neural networks in early detection of melanomas
Research goal:
Extending the knowledge of melanoma classification by exploring areas such as:
- Artifact removal
- Thickness prediction
- General anatomic sites localisation
- Global pattern recognition
- Differt approach to acral lesions
- Confocal microscopy
People involved:
- J. Jaworek-Korjakowska
- A. Wójcicka
- A. Brodzicki
- F. Noworolnik
- D. Kucharski
Computer-aided dermatopathology
Research goal:
To improve skin melanoma diagnosis by providing additional means of automatic detection of important diagnostic features in histopathological images.
The research topics include:
- tissue segmentation
- epidermis segmentation
- nests of melanocytes segmentation
- epidermal morphometry measurement
Team members involved:
- J. Jaworek-Korjakowska
- P. Kłeczek
- D. Kucharski
Anomaly detection with the use of pre-trained CNN architectures
Research goal:
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.
The research topics include:
- data mining and preparation
- data preprocessing
- CNN architectures building
Team members involved:
- M. Piekarski
- J. Jaworek-Korjakowska
Detection and analysis of patterns
Cell detection
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.
The research topics include:
- fluorescence images
- image processing
- sharing information from different images
- convolutional neural networks
Team members involved:
- J. Jaworek-Korjakowska
- A. Brodzicki
Bacteria response clustering
Newly opened project, in cooperation with Stanford University. The main idea is to analyse bacteria reaction in response to different serums.
The research topics include:
- data clustering
- bacteria response analysis
Team members involved:
- J. Jaworek-Korjakowska
- A. Brodzicki
Reconstructing images' missing areas with generative models
The research topics include:
- reconstruction
- generative learning
- GANs
- autoencoders
Team members involved:
- J. Jaworek-Korjakowska
- D. Kucharski
Vehicle interior image segmentation
The research topics include:
- dataset preparation
- image classification
- image segmentation
Team members involved:
- J. Jaworek-Korjakowska
- A. Kostuch
Robotics
The research covers the topic of reinforcement learning used for robotic arm optimal trajectory planning.
Team members involved:
- J. Jaworek-Korjakowska
- M. Aleksandrowicz
Strony
