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192 results on '"Fabijanska, A."'

1. Modified CNN-Watershed for Corneal Endothelium Segmentation: Image-to-Image Versus Sliding-Window Comparison

Author

Kucharski, Adrian, Fabijańska, Anna, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Strumiłło, Paweł, editor, Klepaczko, Artur, editor, Strzelecki, Michał, editor, and Bociąga, Dorota, editor

Published
2024
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4. An Algorithm for Matching Binary Airway Trees in 3D Images

Author

Kucharski, Adrian, Fabijańska, Anna, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Piaseczna, Natalia, editor, Gorczowska, Magdalena, editor, and Łach, Agnieszka, editor

Published
2022
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6. Graph Convolutional Networks for Semi-Supervised Image Segmentation

Author

Anna Fabijanska

Subjects
GCN, graph convolutional networks, graph node clustering, region adjacency graph, semi-supervised image segmentation, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The problem of image segmentation is one of the most significant ones in computer vision. Recently, deep-learning methods have dominated state-of-the-art solutions that automatically or interactively divide an image into subregions. However, the limitation of deep-learning approaches is that they require a substantial amount of training data, which is costly to prepare. An alternative solution is semi-supervised image segmentation. It requires rough denotations to define constraints that are next generalized to precisely delimit relevant image regions without using train examples. Among semi-supervised strategies for image segmentation, the leading are graph-based techniques that define image segmentation as a result of pixel or region affinity graph partitioning. This paper revisits the problem of graph-based image segmentation. It approaches the problem as semi-supervised node classification in the SLIC superpixels region adjacency graph using a graph convolutional network (GCN). The performance of both spectral and spatial graph convolution operators is considered, represented by Chebyshev convolution operator and GraphSAGE respectively. The results of the proposed method applied to binary and multi-label segmentation are presented, numerically assessed, and analyzed. In its best variant, the proposed method scored the average DICE of 0.86 in the binary segmentation task and 0.79 in the multi-label segmentation task. Comparison with state-of-the-art graph-based methods, including Random Walker and GrabCut, shows that graph convolutional networks can represent an attractive alternative to the existing solutions to graph-based semi-supervised image segmentation.

Published
2022
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8. An Algorithm of Pig Segmentation from Top-View Infrared Video Sequences

Author

Kielanowski, Paweł, Fabijańska, Anna, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bartoszewicz, Andrzej, editor, and Kabziński, Jacek, editor

Published
2020
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15. U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework

Author

Fabijańska, Anna, Vacavant, Antoine, Lebre, Marie-Ange, Pavan, Ana L. M., de Pina, Diana R., Abergel, Armand, Chabrot, Pascal, Magnin, Benoît, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Chmielewski, Leszek J., editor, Kozera, Ryszard, editor, Orłowski, Arkadiusz, editor, Wojciechowski, Konrad, editor, Bruckstein, Alfred M., editor, and Petkov, Nicolai, editor

Published
2018
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19. Viral Genome Deep Classifier

Author

Anna Fabijanska and Szymon Grabowski

Subjects
Genome, virus, subtyping, classification, convolutional neural network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The task of virus classification into subtypes is an important concern in many categorization studies, e.g., in virology or epidemiology. Therefore, the problem of virus subtyping has been a subject of considerable interest in the last decade. Although there exist several virus subtyping tools, they are often dedicated to a specific family of viruses. Even specialized methods, however, often fail to correctly subtype viruses, such as HIV or influenza. To address these shortcomings, we present a viral genome deep classifier (VGDC)-a tool for an automatic virus subtyping, which employs a deep convolutional neural network (CNN). The method is universal and can be applied for subtyping any virus, as confirmed by experiments on dengue, hepatitis B and C, HIV-1, and influenza A datasets. For all considered virus types, the obtained classification rates are very high with the corresponding values of the F1-score ranging from about 0.85 to 1.00 depending on the virus type and the considered number of subtypes. For HIV-1 and influenza A, the VGDC significantly outperforms the leading competitors, including CASTOR and COMET. The VGDC source code is freely available to facilitate easy usage and comparison with future approaches.

Published
2019
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21. A Comparative Study of Image Enhancement Methods in Tree-Ring Analysis

Author

Fabijańska, Anna, Danek, Małgorzata, Barniak, Joanna, Piórkowski, Adam, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, and Choraś, Ryszard S., editor

Published
2017
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27. Assessment of the Brain Coverage Ratio in the Postoperative Craniosynostosis Based on 3D CT Scans

Author

Fabijańska, Anna, Gocławski, Jarosław, Mikołajczyk-Wieczorek, Wanda, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, and Choraś, Ryszard S., editor

Published
2016
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28. Airway Segmentation, Skeletonization, and Tree Matching to Improve Registration of 3D CT Images with Large Opacities in the Lungs

Author

Gómez Betancur, Duván Alberto, Fabijańska, Anna, Flórez-Valencia, Leonardo, Morales Pinzón, Alfredo, Dávila Serrano, Eduardo Enrique, Richard, Jean-Christophe, Orkisz, Maciej, Hernández Hoyos, Marcela, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chmielewski, Leszek J., editor, Datta, Amitava, editor, Kozera, Ryszard, editor, and Wojciechowski, Konrad, editor

Published
2016
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42. Automatic Assessment of Skull Circumference in Craniosynostosis

Author

Fabijańska, Anna, Węgliński, Tomasz, Gocławski, Jarosław, Mikołajczyk-Wieczorek, Wanda, Zakrzewski, Krzysztof, Nowosławska, Emilia, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chmielewski, Leszek J., editor, Kozera, Ryszard, editor, Shin, Bok-Suk, editor, and Wojciechowski, Konrad, editor

Published
2014
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