====== Research areas ======

===== Deep neural networks in early detection of melanomas =====

=====  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

++++ Research activity |

Publications:

  * Dariusz Kucharski, Pawel Kleczek, **Joanna Jaworek-Korjakowska**, Grzegorz Dyduch, Marek Gorgon. //Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders//. Sensors, 2020, vol. 20, issue 6, 1546, doi: [[https://doi.org/10.3390/s20061546|10.3390/s20061546]]  ([[https://www.mdpi.com/1424-8220/20/6/1546/htm|HTML]])  \\ [size=80%][IF5 (2018) = 2.737, Top10][/size] 
  * Pawel Kleczek, **Joanna Jaworek-Korjakowska**, Marek Gorgon. //A novel method for tissue segmentation in high-resolution H&E-stained histopathological whole-slide images//. Computerized Medical Imaging and Graphics, 2020, vol. 79, 2022, Art. ID 101686, doi: [[https://doi.org/10.1016/j.compmedimag.2019.101686|10.1016/j.compmedimag.2019.101686]]  ([[https://www.sciencedirect.com/science/article/pii/S0895611119301016|HTML]])  \\ [size=80%][IF5 (2018) = 2.737, Top10][/size] 
  * Pawel Kleczek, Grzegorz Dyduch, Agnieszka Graczyk-Jarzynka, **Joanna Jaworek-Korjakowska**. //A New Approach to Border Irregularity Assessment with Application in Skin Pathology//. Applied Sciences (Basel), 2019, 9(10), 2022, doi: [[https://doi.org/10.3390/app9102022|10.3390/app9102022]]  ([[https://www.mdpi.com/2076-3417/9/10/2022|Abstract]], [[https://www.mdpi.com/2076-3417/9/10/2022/html|HTML]], [[https://www.mdpi.com/2076-3417/9/10/2022/pdf|PDF]]) \\ [size=80%][IF5 (2018) = 2.287][/size]
  * Paweł Kłeczek, Martyna Lech, Grzegorz Dyduch, **Joanna Jaworek-Korjakowska**, Ryszard Tadeusiewicz. //Segmentation of black ink and melanin in skin histopathological images//. Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105811A (2018); doi: [[https://doi.org/10.1117/12.2292859|10.1117/12.2292859]]. ([[https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10581/105811A/Segmentation-of-black-ink-and-melanin-in-skin-histopathological-images/10.1117/12.2292859.short?SSO=1|Abstract]])
  * Paweł Kłeczek, Grzegorz Dyduch, **Joanna Jaworek-Korjakowska**, Ryszard Tadeusiewicz. //Automated epidermis segmentation in histopathological images of human skin stained with hematoxylin and eosin//. Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400M (2017). doi: [[https://doi.org/10.1117/12.2249018|10.1117/12.2249018]]. ([[http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2608559|Abstract]], [[https://drive.google.com/open?id=0B1Rp-jhc52-PQldnMjZNM01tUVk|Poster PDF]])
  * Paweł Kłeczek, Sylwia Mól, **Joanna Jaworek-Korjakowska**. //The Accuracy of H&E Stain Unmixing Techniques When Estimating Relative Stain Concentrations//. PCBBE 2017: Advances in Intelligent Systems and Computing, Springer (2017), doi: [[http://dx.doi.org/10.1007/978-3-319-66905-2_7|10.1007/978-3-319-66905-2_7]], pp. 87--97 ([[https://link.springer.com/chapter/10.1007/978-3-319-66905-2_7|Abstract]])

++++

===== 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.

[[https://synchrotron.uj.edu.pl|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

===== 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

===== 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

===== Reconstructing images' missing areas with generative models =====

The research topics include:
  * reconstruction
  * generative learning
  * GANs
  * autoencoders