Contact:
- email:
- magmilec@pg.edu.pl
Positions:
Assistant professor
- workplace:
- Katedra Inżynierii Biomedycznej
Budynek A Wydziału Elektroniki, Telekomunikacji i Informatyki, EA 106
- phone:
- (58) 348 62 94

Publications:
-
Publication
- Year 2024
This study aimed to develop and assess an application designed to enhance the management of a local client database consisting of mammographic images with a focus on ensuring that images are suitably and uniformly prepared for federated learning applications. The application supports a comprehensive approach, starting with a versatile image-loading function that supports DICOM files from various medical imaging devices and settings....
Full text to download in external service
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Publication
- L. Pedersen
- M. Mazur-Milecka
- J. Rumiński
- S. R. Wagner
- Machine Learning and Knowledge Extraction - Year 2024
Previous reviews have investigated machine learning (ML) models used to predict the risk of developing preeclampsia. However, they have not addressed the intended deployment of these models throughout pregnancy, nor have they detailed feature performance. This study aims to provide an overview of existing ML models and their intended deployment patterns and performance, along with identified features of high importance. This review...
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Publication
- B. Bulińska
- M. Mazur-Milecka
- M. Sławińska
- J. Rumiński
- R. J. Nowicki
- Journal of Fungi - Year 2024
Onychomycosis is a common fungal nail infection that is difficult to diagnose due to its similarity to other nail conditions. Accurate identification is essential for effective treatment. The current gold standard methods include microscopic examination with potassium hydroxide, fungal cultures, and Periodic acid-Schiff biopsy staining. These conventional techniques, however, suffer from high turnover times, variable sensitivity,...
Full text to download in external service
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Publication
- T. Ludwisiak
- M. Mazur-Milecka
- Year 2024
Effective parking management is essential for ad-dressing the challenges of traffic congestion, city logistics, and air pollution in densely populated urban areas. This paper presents an algorithm designed to optimize parking management within city environments. The proposed system leverages deep learning models to accurately detect and classify street elements and events. Various algorithms, including automatic segmentation of...
Full text to download in external service
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Publication
- Year 2024
The objective of this study is to develop and assess a mobile application that leverages artificial intelligence (AI) to support the rehabilitation of individuals with facial nerve paralysis. The application features two primary functionalities: assessing the paralysis severity and facilitating the monitoring of rehabilitation exercises. The AI algorithm employed for this purpose was Google's ML Kit “face-detection”. The classification...
Full text to download in external service