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EN
One of the most popular methods in the diagnosis of breast cancer is fine-needle biopsy without aspiration. Cell nuclei are the most important elements of cancer diagnostics based on cytological images. Therefore, the first step of successful classification of cytological images is effective automatic segmentation of cell nuclei. The aims of our study include (a) development of segmentation methods of cell nuclei based on deep learning techniques, (b) extraction of some morphometric, colorimetric and textural features of individual segmented nuclei, (c) based on the extracted features, construction of effective classifiers for detecting malignant or benign cases. The segmentation methods used in this paper are based on (a) fully convolutional neural networks and (b) the marker-controlled watershed algorithm. For the classification task, seven various classification methods are used. Cell nuclei segmentation achieves 90% accuracy for benign and 86% for malignant nuclei according to the F-score. The maximum accuracy of the classification reached 80.2% to 92.4%, depending on the type (malignant or benign) of cell nuclei. The classification of tumors based on cytological images is an extremely challenging task. However, the obtained results are promising, and it is possible to state that automatic diagnostic methods are competitive to manual ones.
EN
After regaining independence in 1918, Poland faced the task of rebuilding its infrastructure. One of the main achievements, apart from the port of Gdynia, was Stalowa Wola – a city built on a “raw root” with a closed, avant-garde composition, which was largely realized. The appearance of the city was influenced by the doctrines: modernist (including socialist modernism) and socialist realist. The article presents a design procedure model that can be used in the case of designing areas with the morphogenesis of a modernist city. The proposed method uses the theoretical considerations of Aldo Rossi and Jan Gehl and the elements of User Experience Design (UX).
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