The article presents research findings on developing a medical diagnostic system based on electrical impedance tomography technology. One of the key components of this project is developing a method for reconstructing the structure of human lungs using this technology. The authors of the article compared the effectiveness of two different loss functions in training a neural network, which is tasked with accurately replicating the lung structure based on electrical impedance tomography data.
The researchers analyzed various approaches to calculating loss functions, including cosine embedding loss and InfoNCE loss. They compared the results obtained using these two functions to identify which one performs better in lung structure reconstruction. The findings of these studies may have significant implications for the development of diagnostic systems based on electrical impedance tomography and for improving the effectiveness of lung disease diagnosis.
Additionally, the authors discuss potential future directions for the project, including possible applications of the research findings in clinical practice. Future research efforts may focus on optimizing neural network parameters, exploring alternative loss functions, or utilizing advanced machine learning techniques for even more precise lung structure reconstruction. The pursuit of improving such diagnostic systems could lead to significant advancements in the field of medicine, particularly in diagnosing and treating respiratory diseases.
REFERENCJE(15)
1.
Filipowicz, S.F., Rymarczyk, T., (2003). Tomografia impedancyjna, pomiary, konstrukcje i metody tworzenia obrazu: 95-101.
Goh, T. Y., Basah, S. N., Yazid, H., Safar, M. J. A., Saad F. S. A., (2018). ‘Performance Analysis of Image Thresholding: Otsu Technique’. Measurement 114 (1 January 2018): 298–307. https://doi.org/10.1016/j.meas....
Gray, D. M., Owusu, S.K., Van der Zalm M. M., (2021). ‘Chronic Lung Disease in Children: Disease Focused Use of Lung Function’. Current Opinion in Physiology 22 (1 August 2021): 100438. https://doi.org/10.1016/j.coph....
Hasgall,P.A., Gennaro,Di. F., C. Baumgartner, Neufeld, E., Lloyd, B., Gosselin,M. C., Payne, D., Klingenböck, A., Kuster, N., (2022). ,IT’IS Database for thermal and electromagnetic parameters of biological tissues, Version 4.1.
He, K., Zhang,X., Ren, S., Sun, J., (2016). Deep Residual Learning for Image Recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Hsu, Gee-Sern J., Wu, H. Y., Tsai, C. H., Yanushkevich, S., Gavrilova. M. L., (2022). ‘Masked Face Recognition From Synthesis to Reality’. IEEE Access 10 (2022): 37938–52. https://doi.org/10.1109/ACCESS....
Kłosowski, G., Rymarczyk, T., Wójcik, D., (2023). ‘The Use of an LSTM-Based Autoencoder for Measurement Denoising in Process Tomography’. International Journal of Applied Electromagnetics and Mechanics 73, no. 4 (1 January 2023): 339–52. https://doi.org/10.3233/JAE-23....
Kovács, B., Reiner, P., (2019). Akihiro Sugimoto, Deep Metric Learning using Triplet Network, 42nd International Conference on Telecommunications and Signal Processing.
Li, X., Zhang, R., Wang, O., Duan, X., Sun, Y., Wang. J.,(2023). ‘SAR-CGAN: Improved Generative Adversarial Network for EIT Reconstruction of Lung Diseases’. Biomedical Signal Processing and Control 81 (1 March 2023): 104421. https://doi.org/10.1016/j.bspc....
Maciura, Ł., Rymarczyk, T., Wójcik, D., Gauda, K., Kowalski, M., (2024). ‘Deep Learning Correction for Image Reconstruction in Electrical Impedance Tomography Using UNet Model. | Przeglad Elektrotechniczny | EBSCOhost’. Accessed 28 April 2024. https://openurl.ebsco.com/EPDB....
Meerburg, J. J., Veerman, G. D. M., Aliberti, Tiddens. S., H. A. W. M., (2020). ‘Diagnosis and Quantification of Bronchiectasis Using Computed Tomography or Magnetic Resonance Imaging: A Systematic Review’. Respiratory Medicine 170 (1 August 2020): 105954. https://doi.org/10.1016/j.rmed....
Wójcik, D., Rymarczyk, T., Maciura, Ł., Oleszek, M., Adamkiewicz, P., (2024).‘Time Series Recognition with Convolutional and Recursive Neural Networks in BSPM | IEEE Conference Publication | IEEE Xplore’. Accessed 28 April 2024. https://ieeexplore.ieee.org/ab....
Przetwarzamy dane osobowe zbierane podczas odwiedzania serwisu. Realizacja funkcji pozyskiwania informacji o użytkownikach i ich zachowaniu odbywa się poprzez dobrowolnie wprowadzone w formularzach informacje oraz zapisywanie w urządzeniach końcowych plików cookies (tzw. ciasteczka). Dane, w tym pliki cookies, wykorzystywane są w celu realizacji usług, zapewnienia wygodnego korzystania ze strony oraz w celu monitorowania ruchu zgodnie z Polityką prywatności. Dane są także zbierane i przetwarzane przez narzędzie Google Analytics (więcej).
Możesz zmienić ustawienia cookies w swojej przeglądarce. Ograniczenie stosowania plików cookies w konfiguracji przeglądarki może wpłynąć na niektóre funkcjonalności dostępne na stronie.