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    Tytuł:
    Multimodal Ultrasonic Imaging for Breast Cancer Detection
    Autorzy:
    Camacho, J.
    Medina, L.
    Cruza, J. F.
    Moreno, J. M.
    Fritsch, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/176695.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    ultrasound imaging
    ultrasound tomography
    breast cancer
    Opis:
    Ultrasound is used for breast cancer detection as a technique complementary to mammography, the standard screening method. Current practice is based on reflectivity images obtained with conventional instruments by an operator who positions the ultrasonic transducer by hand over the patient’s body. It is a non-ionizing radiation, pain-free and not expensive technique that provides a higher contrast than mammography to discriminate among fluid-filled cysts and solid masses, especially for dense breast tissue. However, results are quite dependent on the operator’s skills, images are difficult to reproduce, and state-of-the-art instruments have a limited resolution and contrast to show micro-calcifications and to discriminate between lesions and the surrounding tissue. In spite of their advantages, these factors have precluded the use of ultrasound for screening. This work approaches the ultrasound-based early detection of breast cancer with a different concept. A ring array with many elements to cover 360. around a hanging breast allows obtaining repeatable and operator-independent coronal slice images. Such an arrangement is well suited for multi-modal imaging that includes reflectivity, compounded, tomography, and phase coherence images for increased specificity in breast cancer detection. Preliminary work carried out with a mechanical emulation of the ring array and a standard breast phantom shows a high resolution and contrast, with an artifact-free capability provided by phase coherence processing.
    Źródło:
    Archives of Acoustics; 2012, 37, 3; 253-260
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Social-cognitive variables as predictors of intention to undergo breast reconstruction
    Autorzy:
    Życińska, Jolanta
    Powiązania:
    https://bibliotekanauki.pl/articles/951972.pdf
    Data publikacji:
    2015-03-01
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    self-efficacy
    outcome expectancies
    intention
    depression
    breast cancer
    Opis:
    The aim of the study was to determine the role of self-efficacy, outcome expectancies, and risk perception (including consequences of mastectomy) in formulating the intention to undergo breast reconstruction in 178 women after total mastectomy. The social-cognitive variables were measured in the context of breast reconstruction, while depression was assessed using the Beck Depression Inventory. The structural equation modeling revealed that among the predictors there were only two that accounted for the intention to undergo breast reconstruction, i.e. self-efficacy and outcome expectancies (R2 = .67). Subsequent analyses of the related moderators, i.e. depression, age, and duration of the disease indicated a good fit to the data. Nevertheless, in subgroups with poorer resources (older age, depression, and longer duration of the disease) the direct effects of self-efficacy on intention were less noticeable or non-existent. The results suggest that self-efficacy may play the regulating role in making a breast reconstruction decision if individual resources are taken into account.
    Źródło:
    Polish Psychological Bulletin; 2015, 46, 1; 88-95
    0079-2993
    Pojawia się w:
    Polish Psychological Bulletin
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Characterization of Ultrasonic Full Angle Spatial Compounding as a Possible Alternative for Breast Cancer Screening
    Autorzy:
    Medina, L.
    Camacho, J.
    Fritsch, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/177766.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    breast cancer
    Full Angle Spatial Compounding
    resolution
    isotropy
    Opis:
    Breast cancer screening is based on X-ray mammography, while ultrasound is considered a complementary technique with improved detection in dense tissue. However, breast cancer screening requires a technique that provides repeatable results at the inspection interval which cannot be achieved with manual breast exploration. During the last years there have appeared several approaches to overcome this limitation by means of automated ultrasonic tomography performed with motorized probes or with a large set of array transducers. This work addresses these problems by considering a quite simple and low-cost arrangement, formed with a ring of conventional medical-grade array probes which are multiplexed to the electronics to build Full Angle Spatially Compounded (FASC) images. The work analyzes the performance of such arrangement in terms of resolution and isotropy, showing by numerical modelling and experimentally that it provides high resolution and homogeneity in the whole imaged region. The implementation of this technique would provide more than one circular FASC per second and a whole breast volume image in 1–2 minutes with conventional technology, a process fast enough to be clinically useful. Moreover, the automated technique is repeatable and can be used by the clinician to perform immediately the diagnosis without requiring additional data processing.
    Źródło:
    Archives of Acoustics; 2015, 40, 3; 301-310
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble of classifiers based on deep learning for medical image recognition
    Autorzy:
    Gil, Fabian
    Osowski, Stanisław
    Świderski, Bartosz
    Słowińska, Monika
    Powiązania:
    https://bibliotekanauki.pl/articles/2203370.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    breast cancer
    CNN
    deep learning
    ensemble of classifiers
    feature selection
    melanoma
    Opis:
    The paper presents special forms of an ensemble of classifiers for analysis of medical images based on application of deep learning. The study analyzes different structures of convolutional neural networks applied in the recognition of two types of medical images: dermoscopic images for melanoma and mammograms for breast cancer. Two approaches to ensemble creation are proposed. In the first approach, the images are processed by a convolutional neural network and the flattened vector of image descriptors is subjected to feature selection by applying different selection methods. As a result, different sets of a limited number of diagnostic features are generated. In the next stage, these sets of features represent input attributes for the classical classifiers: support vector machine, a random forest of decision trees, and softmax. By combining different selection methods with these classifiers an ensemble classification system is created and integrated by majority voting. In the second approach, different structures of convolutional neural networks are directly applied as the members of the ensemble. The efficiency of the proposed classification systems is investigated and compared to medical data representing dermoscopic images of melanoma and breast cancer mammogram images. Thanks to fusion of the results of many classifiers forming an ensemble, accuracy and all other quality measures have been significantly increased for both types of medical images.
    Źródło:
    Metrology and Measurement Systems; 2023, 30, 1; 139--156
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Genetic predisposition to breast and/or ovarian cancer – focus on the candidate BARD1 gene
    Autorzy:
    Klonowska, K.
    Ratajska, M.
    Wojciechowska, M.
    Kozlowski, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/80894.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    breast cancer
    ovarian cancer
    genetic predisposition
    BRCA1 gene
    structure
    function
    BARD1 gene
    Źródło:
    BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology; 2014, 95, 3
    0860-7796
    Pojawia się w:
    BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparative study on the classification methods for breast cancer diagnosis
    Autorzy:
    Qiu, Y.
    Zhou, G.
    Zhao, Q.
    Cichocki, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/200743.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    breast cancer
    mammography
    DDSM
    comparative study
    deep learning
    rak piersi
    mammografia
    Badanie porównawcze
    uczenie głębokie
    Opis:
    Digital mammography is one of the most widely used approaches for breast cancer diagnosis. Many researchers have demonstrated the superiority of machine learning methods in breast cancer diagnosis using different mammography databases. Since these methods often have different pros and cons, which may confuse doctors and researchers, an elaborate comparison and examination among them is urgently needed for practical breast cancer diagnosis. In this study, we conducted a comprehensive comparative study of the state-of-the-art machine learning methods that are promising in breast cancer diagnosis. For this purpose we analyze the largest mammography diagnosis database: Digital Database for Screening Mammography (DDSM). We considered various approaches for feature extraction including principal component analysis (PCA), nonnegative matrix factorization (NMF), spatial-temporal discriminant analysis (STDA) and those for classification including linear discriminant analysis (LDA), random forests (RaF), k-nearest neighbors (kNN), as well as deep learning methods including convolutional neural networks (CNN) and stacked sparse autoencoder (SSAE). This paper can serve as a guideline and useful clues for doctors who are going to select machine learning methods for their breast cancer computer-aided diagnosis (CAD) systems as well for researchers interested in developing more reliable and efficient methods for breast cancer diagnosis.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 841-848
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Reduced expression of AURKA in peripheral blood of breast cancer patients
    Autorzy:
    Goh, L.P.W.
    See, E.U.H.
    Chua, K.H.
    Lee, P.-C.
    Powiązania:
    https://bibliotekanauki.pl/articles/81279.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    breast cancer
    patient
    Aurora kinase
    gene expression
    peripheral blood
    cell cycle
    quantitative real-time polymerase chain reaction
    Źródło:
    BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology; 2018, 99, 1
    0860-7796
    Pojawia się w:
    BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The shRNA-mediated silencing of VEGF-C illustrates its role in proliferation, chemosensitization, tumour colonization, and anchorage independence
    Autorzy:
    Tambe, P.
    Purohit, I.
    Suneja, D.
    More, S.
    Desai, P.
    Shrivastava, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/80325.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    vascular endothelial growth factor
    gene expression
    endothelial cell
    breast cancer
    lymphangiogenesis
    metastasis
    proliferation
    chemosensitivity
    tumour cell
    RNAi
    Źródło:
    BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology; 2015, 96, 3
    0860-7796
    Pojawia się w:
    BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning versus classical neural approach to mammogram recognition
    Autorzy:
    Kurek, J.
    Świderski, B.
    Osowski, S.
    Kruk, M.
    Barhoumi, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/200919.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    convolutional neural networks
    breast cancer diagnosis
    mammogram recognition
    diagnostic features
    splotowe sieci neuronowe
    diagnostyka raka piersi
    rozpoznawanie
    mammografia
    cechy diagnostyczne
    Opis:
    Automatic recognition of mammographic images in breast cancer is a complex issue due to the confusing appearance of some perfectly normal tissues which look like masses. The existing computer-aided systems suffer from non-satisfactory accuracy of cancer detection. This paper addresses this problem and proposes two alternative techniques of mammogram recognition: the application of a variety of methods for definition of numerical image descriptors in combination with an efficient SVM classifier (so-called classical approach) and application of deep learning in the form of convolutional neural networks, enhanced with additional transformations of input mammographic images. The key point of the first approach is defining the proper numerical image descriptors and selecting the set which is the most class discriminative. To achieve better performance of the classifier, many image descriptors were defined by means of applying different characterization of the images: Hilbert curve representation, Kolmogorov-Smirnov statistics, the maximum subregion principle, percolation theory, fractal texture descriptors as well as application of wavelet and wavelet packets. Thanks to them, better description of the basic image properties has been obtained. In the case of deep learning, the features are automatically extracted as part of convolutional neural network learning. To get better quality of results, additional representations of mammograms, in the form of nonnegative matrix factorization and the self-similarity principle, have been proposed. The methods applied were evaluated based on a large database composed of 10,168 regions of interest in mammographic images taken from the DDSM database. Experimental results prove the advantage of deep learning over traditional approach to image recognition. Our best average accuracy in recognizing abnormal cases (malignant plus benign versus healthy) was 85.83%, with sensitivity of 82.82%, specificity of 86.59% and AUC = 0.919. These results are among the best for this massive database.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 831-840
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Emocje w ruchach społecznych na przykładzie ruchów obrony praw ojców i Amazonek w Polsce
    Emotions in Social Movements. The Case of the Fathers’ Rights Movement and the Breast Cancer Survivors Movement in Poland
    Autorzy:
    Wojnicka, Katarzyna
    Zierkiewicz, Edyta
    Powiązania:
    https://bibliotekanauki.pl/articles/427451.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    ruchy społeczne
    emocje
    ruch obrony praw ojców
    ruch polskich Amazonek
    social movements
    emotions
    fathers’ rights movement
    breast cancer survivors movement
    Polska
    Opis:
    Artykuł stanowi pokłosie badań skoncentrowanych na uchwyceniu znaczenia emocji dla formowania, charakteru i dynamiki współczesnych ruchów społecznych na przykładzie polskiego ruchu obrony praw ojców oraz ruchu Amazonek. Tekst oparty został głównie na wynikach badań własnych, przeprowadzonych z użyciem metod badań jakościowych z przedstawicielkami i aktywistami powyższych ruchów w latach 2009–2012. Analiza materiału badawczego poprzedzona jest szkicem na temat statusu emocji w (polskich) badaniach społecznych oraz przeglądem stanu wiedzy dotyczącego prezentowanych fenomenów społecznych w Polsce.
    The article results from research on the role of emotions in the constitution, character and dynamics of contemporary social movements. The paper is based on the cases of the fathers’ rights movement and the breast cancer survivors movement in Poland and is to a large extent the result of qualitative sociological research conducted with activists from these movements between 2009 and 2012. The analysis of the gathered material is preceded by an outline of the status of emotions in (Polish) sociology and the state of affairs in Polish research on the social movements that were the key subject of our study.
    Źródło:
    Studia Socjologiczne; 2014, 4(215); 209-232
    0039-3371
    Pojawia się w:
    Studia Socjologiczne
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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