Contact:
- email:
- milena.sobotka@pg.edu.pl
Positions:
Engineering and Technical Specialist
- workplace:
- Katedra Inżynierii Biomedycznej
Budynek A Wydziału Elektroniki, Telekomunikacji i Informatyki, EA 207
- phone:
- +48 58 347 17 35

Publications:
-
Publication
Recent inpainting methods have demonstrated im-pressive outcomes in filling missing parts of images, especially for reconstructing facial areas obscured by occlusions. However, studies show that these models are not adequately effective in real-world applications, primarily due to data bias and the distribution of faces in images. This research focuses on domain adaptation of the commonly used Labeled Faces in the Wild (LFW) dataset,...
Full text to download in external service
-
Publication
- Year 2024
Lymphocytes, a type of leukocytes, play a vital role in the immune system. The precise quantification, spatial arrangement and phenotypic characterization of lymphocytes within haematological or histopathological images can serve as a diagnostic indicator of a particular lesion. Artificial neural networks, employed for the detection of lymphocytes, not only can provide support to the work of histopathologists but also enable better...
Full text to download in external service
-
Publication
Pregnancy in a life of a woman, is an important time that is connected with both physiological and psychological changes. This paper aims at developing a digital twin application that allows to assess mother’s health risk and help to diagnose them. The system presented in this paper includes models for three health outcomes: maternal health risk level, diagnosis of gestational diabetes mellitus (GDM), and diagnosis of late onset...
Full text to download in external service
-
Publication
- M. Mazur-Milecka
- N. Kowalczyk
- K. Jaguszewska
- D. Zamkowska
- D. Wójcik
- K. Preis
- H. Skov
- S. R. Wagner
- P. Sandager
- M. Sobotka
- J. Rumiński
- Year 2024
This paper describes a research study that investigates the use of machine learning algorithms on synthetic data to classify the risk of developing preeclampsia by pregnant women. Synthetic datasets were generated based on parameter distributions from three real patient studies. Four models were compared: XGBoost, Support Vector Machine (SVM), Random Forest, and Explainable Boosting Machines (EBM). The study found that the XGBoost...
Full text to download in external service
-
Publication
- IEEE Access - Year 2023
Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify...
Full text available to download