Research Article
BibTex RIS Cite

Tomosentez Görüntüleri ile Yapılan Derin Öğrenme Çalışmalarında Kullanılan Görüntü Ön İşleme Yöntemleri Üzerine Bir Literatür Araştırması

Year 2023, Issue: 51, 352 - 367, 31.08.2023
https://doi.org/10.31590/ejosat.1312965

Abstract

Bu makale, tomosentez görüntülerinin derin öğrenme çalışmalarında kullanılmasına odaklanarak, görüntü ön işleme yöntemleri üzerine bir literatür araştırması sunmaktadır. Tomosentez, meme dokusunun 3 boyutlu, kesitsel olarak taranmasını sağlayan gelişmiş bir tıbbi görüntüleme tekniğidir. Bu teknikle elde edilen görüntüler 2 boyutlu mamografilere oranla daha yüksek boyutlu olduğu gibi daha gürültülü de olabilirler. Bu nedenle bu görüntülerin derin öğrenme modellerine uygun hale getirilmesi için ön işleme yapılması gerekmektedir. Bu literatür araştırması, tomosentez görüntülerinde kullanılan farklı ön işleme yöntemlerini ele almaktadır. Öncelikle Tomosentez görüntülerinin özellikleri ve derin öğrenme yöntemleri hakkında bir giriş yapılacaktır. Daha sonra, kullanılan ön işleme yöntemleri arasında yer alan filtreleme, normalizasyon, segmentasyon ve artırma gibi teknikler hakkında yapılan literatür araştırmasına ait bilgi verilecektir. Ayrıca, bu yöntemlerin bir arada kullanıldığı örnekler de incelenecektir. Sonuç olarak, bu makale ile Tomosentez görüntüleri üzerinde derin öğrenme çalışmaları yapmak isteyen araştırmacılara faydalı bir Türkçe kaynak sunmak hedeflenmektedir. Yapılan araştırma, görüntü ön işleme yöntemlerinin doğru seçiminin, derin öğrenme modellerinin performansını önemli ölçüde artırabileceğini göstermektedir.

References

  • Ahmed, L., Iqbal, M. M., Aldabbas, H., Khalid, S., Saleem, Y., & Saeed, S. (2020). Images data practices for semantic segmentation of breast cancer using Deep Neural Network. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-01680-1
  • Alguliyev, R. M., Aliguliyev, R. M., & Abdullayeva, F. J. (2019). The improved LSTM and CNN models for ddos attacks prediction in social media. International Journal of Cyber Warfare and Terrorism, 9(1), 1–18. https://doi.org/10.4018/ijcwt.2019010101
  • Amit, G., Ben-Ari, R., Hadad, O., Monovich, E., Granot, N., & Hashoul, S. (2017). Classification of breast MRI lesions using small-size training sets: Comparison of Deep Learning Approaches. SPIE Proceedings. https://doi.org/10.1117/12.2249981
  • Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends® in Machine Learning, 2(1), 1–127. https://doi.org/10.1561/2200000006
  • Bevilacqua, V., Brunetti, A., Guerriero, A., Trotta, G. F., Telegrafo, M., & Moschetta, M. (2019). A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images. Cognitive Systems Research, 53, 3–19. https://doi.org/10.1016/j.cogsys.2018.04.011
  • Boser, B., LeCun, Y., Denker, J. S. (1989 ). Handwritten Digit Recognition with a Back-Propagation Network.
  • Buda, M., Saha, A., Walsh, R., Ghate, S., Li, N., Swiecicki, A., Lo, J. Y., & Mazurowski, M. A. (2021). A data set and deep learning algorithm for the detection of masses and architectural distortions in digital breast tomosynthesis images. JAMA Network Open, 4(8). https://doi.org/10.1001/jamanetworkopen.2021.19100
  • Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2011). Convolutional neural network committees for handwritten character classification. 2011 International Conference on Document Analysis and Recognition. https://doi.org/10.1109/icdar.2011.229
  • El-Shazli, A. M., Youssef, S. M., & Soliman, A. H. (2022). Intelligent Computer-aided model for efficient diagnosis of digital breast tomosynthesis 3D imaging using Deep Learning. Applied Sciences, 12(11), 5736. https://doi.org/10.3390/app12115736
  • Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/10.1207/s15516709cog1402_1
  • Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5–6), 602–610. https://doi.org/10.1016/j.neunet.2005.06.042
  • Harron, N. A., Osman, N. F., Sulaiman, S. N., Karim, N. K., Ismail, A. P., & Soh, Z. H. (2022). An image denoising model using deep learning for Digital Breast Tomosynthesis Images. 2022 IEEE 13th Control and System Graduate Research Colloquium (ICSGRC). https://doi.org/10.1109/icsgrc55096.2022.9845152
  • Helvie, M. A. (2010). Digital Mammography Imaging: Breast Tomosynthesis and Advanced Applications. Radiologic Clinics of North America, 48(5), 917–929. https://doi.org/10.1016/j.rcl.2010.06.009
  • Hinton, G. E., Osindero, S., & The, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. https://doi.org/10.1162/ neco.2006.18.7.1527
  • Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. CoRR, abs/1207.0580.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Hooley, R. J., Durand, M. A., & Philpotts, L. E. (2017). Advances in Digital Breast Tomosynthesis. American Journal of Roentgenology,208(2),256–266. https://doi.org/10.2214/ajr.16.17127
  • Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11–26. https://doi.org/10.1016/j.neucom.2016.12.038
  • Lu, M. T., Ivanov, A., Mayrhofer, T., Hosny, A., Aerts, H. J., & Hoffmann, U. (2019). Deep learning to assess long-term mortality from chest radiographs. JAMA Network Open, 2(7). https://doi.org/10.1001/jamanetworkopen.2019.7416
  • Memisevic, R., & Hinton, G. E. (2010). Learning to represent spatial transformations with factored higher-order Boltzmann machines. Neural Computation, 22(6), 1473–1492. https://doi.org/10.1162/neco.2010.01-09-953
  • Ren, J., Green, M., & Huang, X. (2021). From traditional to deep learning: Fault diagnosis for Autonomous Vehicles. Learning Control, 205–219. https://doi.org/10.1016/b978-0-12-822314-7.00013-4
  • Ricciardi, R., Mettivier, G., Staffa, M., Sarno, A., Acampora, G., Minelli, S., Santoro, A., Antignani, E., Orientale, A., Pilotti, I. A. M., Santangelo, V., D’Andria, P., & Russo, P. (2021). A deep learning classifier for digital breast tomosynthesis. Physica Medica, 83, 184–193. https://doi.org/10.1016/j.ejmp.2021.03.021
  • Salakhutdinov, R. & Larochelle, H. (2009). Efficient learning of deep Boltzmann machines, in Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, 16‐18 April., Florida, USA, 693‐700.
  • Salakhutdinov, R., & Hinton, G. (2009). Deep Boltzmann machines, in Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, 16–18 April, Florida, USA, 448–455.
  • Salakhutdinov, R., & Murray, I. (2008). On the quantitative analysis of deep belief networks, Proceedings of the 25th international conference on Machine learning ‐ ICML '08, USA, 10–20.
  • Samala, R. K., Chan, H.-P., Hadjiiski, L., Helvie, M. A., Richter, C. D., & Cha, K. H. (2019). Breast cancer diagnosis in digital breast tomosynthesis: Effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Transactions on Medical Imaging, 38(3), 686–696. https://doi.org/10.1109/tmi.2018.2870343
  • Sarker, I. H. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications and Research Directions. SN Computer Science, 2(6). https://doi.org/10.1007/s42979-021-00815-1
  • Schwenzow, J., Hartmann, J., Schikowsky, A. and Heitmann, M. (2021), “Understanding videos at scale: how to extract insights for business research”, Journal of Business Research, Vol. 123, pp. 367-379, doi: 10.1016/j.jbusres.2020.09.059.
  • Sechopoulos, I., Teuwen, J., & Mann, R. (2021). Artificial Intelligence for Breast Cancer Detection in mammography and Digital Breast Tomosynthesis: State of the art. Seminars in Cancer Biology, 72, 214–225. https://doi.org/10.1016/j.semcancer.2020.06.002
  • Shimokawa, D., Takahashi, K., Kurosawa, D., Takaya, E., Oba, K., Yagishita, K., Fukuda, T., Tsunoda, H., & Ueda, T. (2022). Deep learning model for breast cancer diagnosis based on bilateral asymmetrical detection (Bilad) in digital breast tomosynthesis images. Radiological Physics and Technology, 16(1), 20–27. https://doi.org/10.1007/s12194-022-00686-y
  • Shimokawa, D., Takahashi, K., Oba, K., Takaya, E., Usuzaki, T., Kadowaki, M., Kawaguchi, K., Adachi, M., Kaneno, T., Fukuda, T., Yagishita, K., Tsunoda, H., & Ueda, T. (2022). Deep Learning Model for Predicting the Presence of Stromal Invasion of Breast Cancer on Digital Breast Tomosynthesis. https://doi.org/10.21203/rs.3.rs-1807556/v1 Singh, S., Matthews, T. P., Shah, M., Mombourquette, B., Tsue, T., Long, A., Almohsen, R., Pedemonte, S., & Su, J. (2020). Adaptation of a deep learning malignancy model from full-field digital mammography to Digital Breast Tomosynthesis. Medical Imaging 2020: Computer-Aided Diagnosis. https://doi.org/10.1117/12.2549923
  • Skaane, P., Bandos, A. I., Gullien, R., Eben, E. B., Ekseth, U., Haakenaasen, U., Izadi, M., Jebsen, I. N., Jahr, G., Krager, M., Niklason, L. T., Hofvind, S., & Gur, D. (2013). Comparison of Digital Mammography alone and Digital Mammography Plus Tomosynthesis in a population-based screening program. Radiology, 267(1), 47–56. https://doi.org/10.1148/radiol.12121373
  • Usuga Cadavid, J.P., Grabot, B., Lamouri, S., Pellerin, R. and Fortin, A. (2022), “Valuing free-form text data from maintenance logs through transfer learning with CamemBERT”, Enterprise Information Systems, Vol. 16 No. 6, pp. 1-29, 1790043, doi: 10.1080/17517575.2020.1790043.
  • Usuga-Cadavid, J.P., Lamouri, S., Grabot, B. and Fortin, A. (2022), “Using deep learning to value freeform text data for predictive maintenance”, International Journal of Production Research, Vol. 60 No. 14, pp. 4548-4575, doi: 10.1080/00207543.2021.1951868.
  • Vedantham, S., Karellas, A., Vijayaraghavan, G. R., & Kopans, D. B. (2015). Digital Breast Tomosynthesis: State of the art. Radiology, 277(3), 663–684. https://doi.org/10.1148/radiol.2015141303
  • Yousefi, M., Krzyżak, A., & Suen, C. Y. (2018). Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning. Computers in Biology and Medicine, 96, 283–293. https://doi.org/10.1016/j.compbiomed.2018.04.004
  • Zhang, X., Zhang, Y., Han, E. Y., Jacobs, N., Han, Q., Wang, X., & Liu, J. (2018). Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Transactions on NanoBioscience, 17(3), 237–242. https://doi.org/10.1109/tnb.2018.2845103
  • Zhang, Y., Wang, X., Blanton, H., Liang, G., Xing, X., & Jacobs, N. (2019). 2d Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). https://doi.org/10.1109/bibm47256.2019.8983097
  • Zhao, B., Zhang, X., Li, H. and Yang, Z. (2020), “Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions”, KnowledgeBased Systems, Vol. 199, 105971, doi: 10.1016/j.knosys.2020.105971.
  • Zorzi, M., Testolin, A., & Stoianov, I. P. (2013). Modeling language and cognition with deep unsupervised learning: A tutorial overview. Frontiers in Psychology, 4(1), 515–527.

A Literature Review on Image Preprocessing Methods Used in Deep Learning Studies Using Tomosynthesis Images

Year 2023, Issue: 51, 352 - 367, 31.08.2023
https://doi.org/10.31590/ejosat.1312965

Abstract

This article presents a literature review on image preprocessing methods, focusing on the use of tomosynthesis images in deep learning studies. Tomosynthesis is an advanced medical imaging technique that provides 3-dimensional, cross-sectional scanning of breast tissue. The images obtained with this technique can be higher dimensional and noisier than 2D mammograms. Therefore, preprocessing is required to make these images suitable for deep learning models. This literature review addresses the different preprocessing methods used in tomosynthesis images. First of all, an introduction will be made about the properties of Tomosynthesis images and deep learning methods. Then, information about the techniques such as filtering, normalization, segmentation and augmentation, which are among the preprocessing methods used, will be given from the literature search. In addition, examples where these methods are used together will also be examined. In conclusion, with this article, it is aimed to present a useful Turkish resource to researchers who want to do deep learning studies on Tomosynthesis images. The research shows that the right choice of image preprocessing methods can significantly improve the performance of deep learning models.

References

  • Ahmed, L., Iqbal, M. M., Aldabbas, H., Khalid, S., Saleem, Y., & Saeed, S. (2020). Images data practices for semantic segmentation of breast cancer using Deep Neural Network. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-01680-1
  • Alguliyev, R. M., Aliguliyev, R. M., & Abdullayeva, F. J. (2019). The improved LSTM and CNN models for ddos attacks prediction in social media. International Journal of Cyber Warfare and Terrorism, 9(1), 1–18. https://doi.org/10.4018/ijcwt.2019010101
  • Amit, G., Ben-Ari, R., Hadad, O., Monovich, E., Granot, N., & Hashoul, S. (2017). Classification of breast MRI lesions using small-size training sets: Comparison of Deep Learning Approaches. SPIE Proceedings. https://doi.org/10.1117/12.2249981
  • Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends® in Machine Learning, 2(1), 1–127. https://doi.org/10.1561/2200000006
  • Bevilacqua, V., Brunetti, A., Guerriero, A., Trotta, G. F., Telegrafo, M., & Moschetta, M. (2019). A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images. Cognitive Systems Research, 53, 3–19. https://doi.org/10.1016/j.cogsys.2018.04.011
  • Boser, B., LeCun, Y., Denker, J. S. (1989 ). Handwritten Digit Recognition with a Back-Propagation Network.
  • Buda, M., Saha, A., Walsh, R., Ghate, S., Li, N., Swiecicki, A., Lo, J. Y., & Mazurowski, M. A. (2021). A data set and deep learning algorithm for the detection of masses and architectural distortions in digital breast tomosynthesis images. JAMA Network Open, 4(8). https://doi.org/10.1001/jamanetworkopen.2021.19100
  • Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2011). Convolutional neural network committees for handwritten character classification. 2011 International Conference on Document Analysis and Recognition. https://doi.org/10.1109/icdar.2011.229
  • El-Shazli, A. M., Youssef, S. M., & Soliman, A. H. (2022). Intelligent Computer-aided model for efficient diagnosis of digital breast tomosynthesis 3D imaging using Deep Learning. Applied Sciences, 12(11), 5736. https://doi.org/10.3390/app12115736
  • Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/10.1207/s15516709cog1402_1
  • Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5–6), 602–610. https://doi.org/10.1016/j.neunet.2005.06.042
  • Harron, N. A., Osman, N. F., Sulaiman, S. N., Karim, N. K., Ismail, A. P., & Soh, Z. H. (2022). An image denoising model using deep learning for Digital Breast Tomosynthesis Images. 2022 IEEE 13th Control and System Graduate Research Colloquium (ICSGRC). https://doi.org/10.1109/icsgrc55096.2022.9845152
  • Helvie, M. A. (2010). Digital Mammography Imaging: Breast Tomosynthesis and Advanced Applications. Radiologic Clinics of North America, 48(5), 917–929. https://doi.org/10.1016/j.rcl.2010.06.009
  • Hinton, G. E., Osindero, S., & The, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. https://doi.org/10.1162/ neco.2006.18.7.1527
  • Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. CoRR, abs/1207.0580.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Hooley, R. J., Durand, M. A., & Philpotts, L. E. (2017). Advances in Digital Breast Tomosynthesis. American Journal of Roentgenology,208(2),256–266. https://doi.org/10.2214/ajr.16.17127
  • Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11–26. https://doi.org/10.1016/j.neucom.2016.12.038
  • Lu, M. T., Ivanov, A., Mayrhofer, T., Hosny, A., Aerts, H. J., & Hoffmann, U. (2019). Deep learning to assess long-term mortality from chest radiographs. JAMA Network Open, 2(7). https://doi.org/10.1001/jamanetworkopen.2019.7416
  • Memisevic, R., & Hinton, G. E. (2010). Learning to represent spatial transformations with factored higher-order Boltzmann machines. Neural Computation, 22(6), 1473–1492. https://doi.org/10.1162/neco.2010.01-09-953
  • Ren, J., Green, M., & Huang, X. (2021). From traditional to deep learning: Fault diagnosis for Autonomous Vehicles. Learning Control, 205–219. https://doi.org/10.1016/b978-0-12-822314-7.00013-4
  • Ricciardi, R., Mettivier, G., Staffa, M., Sarno, A., Acampora, G., Minelli, S., Santoro, A., Antignani, E., Orientale, A., Pilotti, I. A. M., Santangelo, V., D’Andria, P., & Russo, P. (2021). A deep learning classifier for digital breast tomosynthesis. Physica Medica, 83, 184–193. https://doi.org/10.1016/j.ejmp.2021.03.021
  • Salakhutdinov, R. & Larochelle, H. (2009). Efficient learning of deep Boltzmann machines, in Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, 16‐18 April., Florida, USA, 693‐700.
  • Salakhutdinov, R., & Hinton, G. (2009). Deep Boltzmann machines, in Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, 16–18 April, Florida, USA, 448–455.
  • Salakhutdinov, R., & Murray, I. (2008). On the quantitative analysis of deep belief networks, Proceedings of the 25th international conference on Machine learning ‐ ICML '08, USA, 10–20.
  • Samala, R. K., Chan, H.-P., Hadjiiski, L., Helvie, M. A., Richter, C. D., & Cha, K. H. (2019). Breast cancer diagnosis in digital breast tomosynthesis: Effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Transactions on Medical Imaging, 38(3), 686–696. https://doi.org/10.1109/tmi.2018.2870343
  • Sarker, I. H. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications and Research Directions. SN Computer Science, 2(6). https://doi.org/10.1007/s42979-021-00815-1
  • Schwenzow, J., Hartmann, J., Schikowsky, A. and Heitmann, M. (2021), “Understanding videos at scale: how to extract insights for business research”, Journal of Business Research, Vol. 123, pp. 367-379, doi: 10.1016/j.jbusres.2020.09.059.
  • Sechopoulos, I., Teuwen, J., & Mann, R. (2021). Artificial Intelligence for Breast Cancer Detection in mammography and Digital Breast Tomosynthesis: State of the art. Seminars in Cancer Biology, 72, 214–225. https://doi.org/10.1016/j.semcancer.2020.06.002
  • Shimokawa, D., Takahashi, K., Kurosawa, D., Takaya, E., Oba, K., Yagishita, K., Fukuda, T., Tsunoda, H., & Ueda, T. (2022). Deep learning model for breast cancer diagnosis based on bilateral asymmetrical detection (Bilad) in digital breast tomosynthesis images. Radiological Physics and Technology, 16(1), 20–27. https://doi.org/10.1007/s12194-022-00686-y
  • Shimokawa, D., Takahashi, K., Oba, K., Takaya, E., Usuzaki, T., Kadowaki, M., Kawaguchi, K., Adachi, M., Kaneno, T., Fukuda, T., Yagishita, K., Tsunoda, H., & Ueda, T. (2022). Deep Learning Model for Predicting the Presence of Stromal Invasion of Breast Cancer on Digital Breast Tomosynthesis. https://doi.org/10.21203/rs.3.rs-1807556/v1 Singh, S., Matthews, T. P., Shah, M., Mombourquette, B., Tsue, T., Long, A., Almohsen, R., Pedemonte, S., & Su, J. (2020). Adaptation of a deep learning malignancy model from full-field digital mammography to Digital Breast Tomosynthesis. Medical Imaging 2020: Computer-Aided Diagnosis. https://doi.org/10.1117/12.2549923
  • Skaane, P., Bandos, A. I., Gullien, R., Eben, E. B., Ekseth, U., Haakenaasen, U., Izadi, M., Jebsen, I. N., Jahr, G., Krager, M., Niklason, L. T., Hofvind, S., & Gur, D. (2013). Comparison of Digital Mammography alone and Digital Mammography Plus Tomosynthesis in a population-based screening program. Radiology, 267(1), 47–56. https://doi.org/10.1148/radiol.12121373
  • Usuga Cadavid, J.P., Grabot, B., Lamouri, S., Pellerin, R. and Fortin, A. (2022), “Valuing free-form text data from maintenance logs through transfer learning with CamemBERT”, Enterprise Information Systems, Vol. 16 No. 6, pp. 1-29, 1790043, doi: 10.1080/17517575.2020.1790043.
  • Usuga-Cadavid, J.P., Lamouri, S., Grabot, B. and Fortin, A. (2022), “Using deep learning to value freeform text data for predictive maintenance”, International Journal of Production Research, Vol. 60 No. 14, pp. 4548-4575, doi: 10.1080/00207543.2021.1951868.
  • Vedantham, S., Karellas, A., Vijayaraghavan, G. R., & Kopans, D. B. (2015). Digital Breast Tomosynthesis: State of the art. Radiology, 277(3), 663–684. https://doi.org/10.1148/radiol.2015141303
  • Yousefi, M., Krzyżak, A., & Suen, C. Y. (2018). Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning. Computers in Biology and Medicine, 96, 283–293. https://doi.org/10.1016/j.compbiomed.2018.04.004
  • Zhang, X., Zhang, Y., Han, E. Y., Jacobs, N., Han, Q., Wang, X., & Liu, J. (2018). Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Transactions on NanoBioscience, 17(3), 237–242. https://doi.org/10.1109/tnb.2018.2845103
  • Zhang, Y., Wang, X., Blanton, H., Liang, G., Xing, X., & Jacobs, N. (2019). 2d Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). https://doi.org/10.1109/bibm47256.2019.8983097
  • Zhao, B., Zhang, X., Li, H. and Yang, Z. (2020), “Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions”, KnowledgeBased Systems, Vol. 199, 105971, doi: 10.1016/j.knosys.2020.105971.
  • Zorzi, M., Testolin, A., & Stoianov, I. P. (2013). Modeling language and cognition with deep unsupervised learning: A tutorial overview. Frontiers in Psychology, 4(1), 515–527.
There are 40 citations in total.

Details

Primary Language Turkish
Subjects Image Processing, Deep Learning
Journal Section Articles
Authors

Elif Aydıngöz 0009-0005-2143-6326

Mert Bal 0000-0001-6250-929X

Early Pub Date September 10, 2023
Publication Date August 31, 2023
Published in Issue Year 2023 Issue: 51

Cite

APA Aydıngöz, E., & Bal, M. (2023). Tomosentez Görüntüleri ile Yapılan Derin Öğrenme Çalışmalarında Kullanılan Görüntü Ön İşleme Yöntemleri Üzerine Bir Literatür Araştırması. Avrupa Bilim Ve Teknoloji Dergisi(51), 352-367. https://doi.org/10.31590/ejosat.1312965