Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 7 Sayı: 3, 280 - 288, 06.12.2020
https://doi.org/10.30897/ijegeo.710913

Öz

Kaynakça

  • Aydın, E., Yüksel, S. E. (2017, May). Buried target detection with ground penetrating radar using deep learning method. In 2017 25th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • Aydın, E., Yüksel, S. E. (2018, May). Transfer and multitask learning method for buried wire detection via GPR. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • Aydın, E., Yüksel, S. E. (2019, May). Transfer and multitask learning using convolutional neural networks for buried wire detection from ground penetrating radar data. In Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV (Vol. 11012, p. 110120Y). International Society for Optics and Photonics.
  • Aykanat, M., Kılıç, Ö., Kurt, B., and Saryal, S. (2017). Classification of lung sounds using convolutional neural networks. EURASIP Journal on Image and Video Processing, 2017(1), 65.
  • Bardou, D., Zhang, K., and Ahmad, S. M. (2018). Lung sounds classification using convolutional neural networks. Artificial intelligence in medicine, 88, 58-69.
  • Bien, N., Rajpurkar, P., Ball, R. L., Irvin, J., Park, A., Jones, E., and Halabi, S. (2018). Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS medicine, 15(11), e1002699.
  • Corey, K. M., Kashyap, S., Lorenzi, E., Lagoo-Deenadayalan, S. A., Heller, K., Whalen, and K., Sendak, M. (2018). Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study. PLoS medicine, 15(11).
  • De Felice, M. (2017). Which deep learning network is best for you? IDG Communications. [Online]. Available: http:// www.cio.com/ article/ 3193689/ artificial-intelligence/whichdeep-learning-network-is-best-for-you.html
  • Drönner, J., Korfhage, N., Egli, S., Mühling, M., Thies, B., Bendix, J., Freisleben, and Seeger, B. (2018). Fast cloud segmentation using convolutional neural networks. Remote Sensing, 10(11), 1782.
  • Fu, K., Li, Y., Sun, H., Yang, X., Xu, G., Li, Y., Sun, X. (2018). A ship rotation detection model in remote sensing images based on feature fusion pyramid network and deep reinforcement learning. Remote Sensing, 10(12), 1922.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville and Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).
  • Gregor, K., Danihelka, I., Graves, A., Rezende, D. J. and Wierstra, D. (2015). Draw: A recurrent neural network for image generation. arXiv preprint arXiv:1502.04623.
  • Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., and Bird, L. M. (2019). Identifying facial phenotypes of genetic disorders using deep learning. Nature medicine, 25(1), 60-64.
  • He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A. and Aerts, H. J. (2018). Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS medicine, 15(11), e1002711.
  • Khellal, A., Ma, H., Fei, Q. (2018). Convolutional neural network based on extreme learning machine for maritime ships recognition in infrared images. Sensors, 18(5), 1490.
  • Kingma, D. P., Welling M. (2014). Auto-encoding Variational Bayes. ICLR.
  • Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
  • Larochele, H., Murray, I. (2011, June). The neural autoregressive distribution estimator. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (pp. 29-37).
  • LeCun, Y., Bottou, L., Bengio, Y., Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
  • Lee, S., Mohr, N. M., Street, W. N. and Nadkarni, P. (2019). Machine learning in relation to emergency medicine clinical and operational scenarios: an overview. Western Journal of Emergency Medicine, 20(2), 219.
  • Lennon M. (2018). A Soldier's Load: Machine Learning In Defense And Other Industries. [Online]. Available: https://www.digitalistmag.com/future-of-work/2018/09/04/soldiers-load-machinelearning-in-defense-other-industries-06184883
  • Ouala, S., Fablet, R., Herzet, C., Chapron, B., Pascual, A., Collard, F., Gaultier, L. (2018). Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature. Remote Sensing, 10(12), 1864.
  • Pham, H. H. N., Futakuchi, M., Bychkov, A., Furukawa, T., Kuroda, K. and Fukuoka, J. (2019). Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach. The American journal of pathology, 189(12), 2428-2439.
  • Radford, A., Metz, L., Chintala, S. (2016). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. ICLR.
  • Rainey, K., Reeder, J. D., Corelli, A. G. (2016, May). Convolution neural networks for ship type recognition. In Automatic Target Recognition XXVI (Vol. 9844, p. 984409). International Society for Optics and Photonics.
  • Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Patel, B. N. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS medicine, 15(11), e1002686.
  • Rezende, D. J., Mohamed, S., Wierstra, D. (2014). Stochastic Backpropagation and Approximate Inference in Deep Generative Models. ICML, 1278–1286.
  • Salman, M, (2018). Fusion of Hyperspectral and Lidar Datasets with Feature and Decision Based Methods and Classification with Deep Convolutional Neural Networks. (Master's thesis). Hacettepe University, Ankara, Türkiye.
  • Salman M. Yuksel, S.E. (2018, May). Fusion of hyperspectral image and LiDAR data and classification using deep convolutional neural networks. IEEE Signal Processing and Communications Applications Conference (SIU), İzmir, Turkey, 2-5 May.
  • Saria, S., Butte, A., Sheikh, A. (2018). Better medicine through machine learning: What’s real, and what’s artificial?. PLoS medicine, 15(12).
  • Schiff, G. D., Volk, L. A., Volodarskaya, M., Williams, D. H., Walsh, L., Myers, S. G., Rozenblum, R. (2017). Screening for medication errors using an outlier detection system. Journal of the American Medical Informatics Association, 24(2), 281-287.
  • Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I. and Guizani, M. (2019). Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges. IEEE Access, 7, 48572-48634.
  • Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Suzuki, K. (2017). Overview of deep learning in medical imaging. Radiological physics and technology, 10(3), 257-273.
  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
  • Theis, L., Bethge, M. (2015). Generative image modeling using spatial lstms. In Advances in Neural Information Processing Systems (pp. 1927-1935).
  • Van den Oord, A., Kalchbrenner, N., Kavukcuoglu, K. (2016a). Pixel Recurrent Neural Networks, ICML, 1747–1756.
  • Van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., Kavukcuoglu, K. (2016b). Conditional Image Generation with PixelCNN Decoders, NIPS.
  • Wang, C., Zhang, H., Wu, F., Zhang, B., Tian, S. (2017, July). Ship classification with deep learning using COSMO-SkyMed SAR data. In IEEE 2017 Geoscience and Remote Sensing Symposium (IGARSS), pp. 558-561.
  • Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., Guo, Z. (2018). Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote Sensing, 10(1), 132.
  • Zheng, L., Wang, O., Hao, S. Ye, C., Liu, M., et al. (2020). Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records. Transl Psychiatry 10, 72.
  • Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8-36.

Deep Learning for Medicine and Remote Sensing: A Brief Review

Yıl 2020, Cilt: 7 Sayı: 3, 280 - 288, 06.12.2020
https://doi.org/10.30897/ijegeo.710913

Öz

In recent years, deep learning methods have come to the forefront in many areas that require remote sensing, from medicine to agriculture, from defense industry to space research; and these methods have given more successful results as compared to traditional methods. The major difference between deep learning and classical recognition methods is that deep learning methods consider an end-to-end learning scheme which gives rise to learning features from raw data. In this study, we discuss the remote sensing problems and how deep learning can be used to solve these problems with a special focus on medical and defense applications. In particular, we review architectures within the deep learning literature and their use cases.

Kaynakça

  • Aydın, E., Yüksel, S. E. (2017, May). Buried target detection with ground penetrating radar using deep learning method. In 2017 25th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • Aydın, E., Yüksel, S. E. (2018, May). Transfer and multitask learning method for buried wire detection via GPR. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • Aydın, E., Yüksel, S. E. (2019, May). Transfer and multitask learning using convolutional neural networks for buried wire detection from ground penetrating radar data. In Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV (Vol. 11012, p. 110120Y). International Society for Optics and Photonics.
  • Aykanat, M., Kılıç, Ö., Kurt, B., and Saryal, S. (2017). Classification of lung sounds using convolutional neural networks. EURASIP Journal on Image and Video Processing, 2017(1), 65.
  • Bardou, D., Zhang, K., and Ahmad, S. M. (2018). Lung sounds classification using convolutional neural networks. Artificial intelligence in medicine, 88, 58-69.
  • Bien, N., Rajpurkar, P., Ball, R. L., Irvin, J., Park, A., Jones, E., and Halabi, S. (2018). Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS medicine, 15(11), e1002699.
  • Corey, K. M., Kashyap, S., Lorenzi, E., Lagoo-Deenadayalan, S. A., Heller, K., Whalen, and K., Sendak, M. (2018). Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study. PLoS medicine, 15(11).
  • De Felice, M. (2017). Which deep learning network is best for you? IDG Communications. [Online]. Available: http:// www.cio.com/ article/ 3193689/ artificial-intelligence/whichdeep-learning-network-is-best-for-you.html
  • Drönner, J., Korfhage, N., Egli, S., Mühling, M., Thies, B., Bendix, J., Freisleben, and Seeger, B. (2018). Fast cloud segmentation using convolutional neural networks. Remote Sensing, 10(11), 1782.
  • Fu, K., Li, Y., Sun, H., Yang, X., Xu, G., Li, Y., Sun, X. (2018). A ship rotation detection model in remote sensing images based on feature fusion pyramid network and deep reinforcement learning. Remote Sensing, 10(12), 1922.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville and Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).
  • Gregor, K., Danihelka, I., Graves, A., Rezende, D. J. and Wierstra, D. (2015). Draw: A recurrent neural network for image generation. arXiv preprint arXiv:1502.04623.
  • Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., and Bird, L. M. (2019). Identifying facial phenotypes of genetic disorders using deep learning. Nature medicine, 25(1), 60-64.
  • He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A. and Aerts, H. J. (2018). Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS medicine, 15(11), e1002711.
  • Khellal, A., Ma, H., Fei, Q. (2018). Convolutional neural network based on extreme learning machine for maritime ships recognition in infrared images. Sensors, 18(5), 1490.
  • Kingma, D. P., Welling M. (2014). Auto-encoding Variational Bayes. ICLR.
  • Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
  • Larochele, H., Murray, I. (2011, June). The neural autoregressive distribution estimator. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (pp. 29-37).
  • LeCun, Y., Bottou, L., Bengio, Y., Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
  • Lee, S., Mohr, N. M., Street, W. N. and Nadkarni, P. (2019). Machine learning in relation to emergency medicine clinical and operational scenarios: an overview. Western Journal of Emergency Medicine, 20(2), 219.
  • Lennon M. (2018). A Soldier's Load: Machine Learning In Defense And Other Industries. [Online]. Available: https://www.digitalistmag.com/future-of-work/2018/09/04/soldiers-load-machinelearning-in-defense-other-industries-06184883
  • Ouala, S., Fablet, R., Herzet, C., Chapron, B., Pascual, A., Collard, F., Gaultier, L. (2018). Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature. Remote Sensing, 10(12), 1864.
  • Pham, H. H. N., Futakuchi, M., Bychkov, A., Furukawa, T., Kuroda, K. and Fukuoka, J. (2019). Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach. The American journal of pathology, 189(12), 2428-2439.
  • Radford, A., Metz, L., Chintala, S. (2016). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. ICLR.
  • Rainey, K., Reeder, J. D., Corelli, A. G. (2016, May). Convolution neural networks for ship type recognition. In Automatic Target Recognition XXVI (Vol. 9844, p. 984409). International Society for Optics and Photonics.
  • Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Patel, B. N. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS medicine, 15(11), e1002686.
  • Rezende, D. J., Mohamed, S., Wierstra, D. (2014). Stochastic Backpropagation and Approximate Inference in Deep Generative Models. ICML, 1278–1286.
  • Salman, M, (2018). Fusion of Hyperspectral and Lidar Datasets with Feature and Decision Based Methods and Classification with Deep Convolutional Neural Networks. (Master's thesis). Hacettepe University, Ankara, Türkiye.
  • Salman M. Yuksel, S.E. (2018, May). Fusion of hyperspectral image and LiDAR data and classification using deep convolutional neural networks. IEEE Signal Processing and Communications Applications Conference (SIU), İzmir, Turkey, 2-5 May.
  • Saria, S., Butte, A., Sheikh, A. (2018). Better medicine through machine learning: What’s real, and what’s artificial?. PLoS medicine, 15(12).
  • Schiff, G. D., Volk, L. A., Volodarskaya, M., Williams, D. H., Walsh, L., Myers, S. G., Rozenblum, R. (2017). Screening for medication errors using an outlier detection system. Journal of the American Medical Informatics Association, 24(2), 281-287.
  • Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I. and Guizani, M. (2019). Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges. IEEE Access, 7, 48572-48634.
  • Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Suzuki, K. (2017). Overview of deep learning in medical imaging. Radiological physics and technology, 10(3), 257-273.
  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
  • Theis, L., Bethge, M. (2015). Generative image modeling using spatial lstms. In Advances in Neural Information Processing Systems (pp. 1927-1935).
  • Van den Oord, A., Kalchbrenner, N., Kavukcuoglu, K. (2016a). Pixel Recurrent Neural Networks, ICML, 1747–1756.
  • Van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., Kavukcuoglu, K. (2016b). Conditional Image Generation with PixelCNN Decoders, NIPS.
  • Wang, C., Zhang, H., Wu, F., Zhang, B., Tian, S. (2017, July). Ship classification with deep learning using COSMO-SkyMed SAR data. In IEEE 2017 Geoscience and Remote Sensing Symposium (IGARSS), pp. 558-561.
  • Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., Guo, Z. (2018). Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote Sensing, 10(1), 132.
  • Zheng, L., Wang, O., Hao, S. Ye, C., Liu, M., et al. (2020). Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records. Transl Psychiatry 10, 72.
  • Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8-36.
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Mehmet Eren Yüksel 0000-0002-7110-0717

Sefa Küçük 0000-0002-0279-3185

Seniha Esen Yüksel 0000-0002-8868-1132

Erkut Erdem 0000-0002-6744-8614

Yayımlanma Tarihi 6 Aralık 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 7 Sayı: 3

Kaynak Göster

APA Yüksel, M. E., Küçük, S., Yüksel, S. E., Erdem, E. (2020). Deep Learning for Medicine and Remote Sensing: A Brief Review. International Journal of Environment and Geoinformatics, 7(3), 280-288. https://doi.org/10.30897/ijegeo.710913