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This paper presents a new approach for effective segmentation of images that can be integrated into any model and methodology; the paradigm that we choose is classification of medical images (3-D chest CT scans) for Covid-19 detection.
2010
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2017
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Kollias, D., Yu, M., Tagaris, A., Leontidis, G., Stafylopatis, A., Kollias, S.: Adaptation and contextualization of deep neural network models. In: 2017 IEEE symposium series on computational intelligence (SSCI). pp. 1–8. IEEE
2017
Earlier work this paper cites.
Tagaris, A., Kollias, D., Stafylopatis, A.: Assessment of parkinson’s disease based on deep neural networks. In: Engineering Applications of Neural Networks: 18th International Conference, EANN 2017, Athens, Greece, August 25–27, 2017, Proceedings. pp. 391–403. Springer (2017)
2017
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2017
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Kollias, D., Tagaris, A., Stafylopatis, A., Kollias, S., Tagaris, G.: Deep neural architectures for prediction in healthcare. Complex & Intelligent Systems 4
2018
Earlier work this paper cites.
Tagaris, A., Kollias, D., Stafylopatis, A., Tagaris, G., Kollias, S.: Machine learning for neurodegenerative disorder diagnosis—survey of practices and launch of benchmark dataset. International Journal on Artificial Intelligence Tools 27
2018
Earlier work this paper cites.
Azad, R., Asadi-Aghbolaghi, M., Fathy, M., Escalera, S.: Bi-directional convlstm u-net with densley connected convolutions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (Oct 2019)
2019
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Amyar, A., Modzelewski, R., Li, H., Ruan, S.: Multi-task deep learning based ct imaging analysis for covid-19 pneumonia: Classification and segmentation. Computers in Biology and Medicine 126
2020
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Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., Kaur, M.: Classification of the covid-19 infected patients using densenet201 based deep transfer learning. Journal of Biomolecular Structure and Dynamics pp. 1–8 (2020)
2020
Earlier work this paper cites.
Khadidos, A., Khadidos, A.O., Kannan, S., Natarajan, Y., Mohanty, S.N., Tsaramirsis, G.: Analysis of covid-19 infections on a ct image using deepsense model. Frontiers in Public Health 8
2020
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2020
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Kollias, D., Vlaxos, Y., Seferis, M., Kollia, I., Sukissian, L., Wingate, J., Kollias, S.D.: Transparent adaptation in deep medical image diagnosis. In: TAILOR. pp. 251–267 (2020)
2020
Earlier work this paper cites.
Morozov, S.P., Andreychenko, A.E., Blokhin, I.A., Gelezhe, P.B., Gonchar, A.P., Nikolaev, A.E., Pavlov, N.A., Chernina, V.Y., Gombolevskiy, V.A.: Mosmeddata: data set of 1110 chest ct scans performed during the covid-19 epidemic. Digital Diagnostics 1
2020
Earlier work this paper cites.
Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Zheng, C.: A weakly-supervised framework for covid-19 classification and lesion localization from chest ct. IEEE transactions on medical imaging 39
2020
Earlier work this paper cites.
He, X., Wang, S., Chu, X., Shi, S., Tang, J., Liu, X., Yan, C., Zhang, J., Ding, G.: Automated model design and benchmarking of deep learning models for covid-19 detection with chest ct scans. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 4821–4829 (2021)
2021
Cited alongside, same era.
Kollias, D., Arsenos, A., Soukissian, L., Kollias, S.: Mia-cov19d: Covid-19 detection through 3-d chest ct image analysis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 537–544 (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 139, pp. 8748–8763. PMLR (18–24 Jul 2021), https://proceedings.mlr.press/v139/radford21a.html
2021
Cited alongside, same era.
Kollias, D., Arsenos, A., Kollias, S.: A deep neural architecture for harmonizing 3-d input data analysis and decision making in medical imaging. Neurocomputing 542
2023
Later among the works it cites.
Kollias, D., Vendal, K., Gadhavi, P., Russom, S.: Btdnet: A multi-modal approach for brain tumor radiogenomic classification. Applied Sciences 13
2023
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2023
Later among the works it cites.
Mazurowski, M.A., Dong, H., Gu, H., Yang, J., Konz, N., Zhang, Y.: Segment anything model for medical image analysis: an experimental study. Medical image analysis 89
2023
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2021
Cited alongside, same era.
Arsenos, A., Kollias, D., Kollias, S.: A large imaging database and novel deep neural architecture for covid-19 diagnosis. In: 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). p. 1–5. IEEE (2022)
2022
Cited alongside, same era.
He, X., Ying, G., Zhang, J., Chu, X.: Evolutionary multi-objective architecture search framework: Application to covid-19 3d ct classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. pp. 560–570. Springer Nature Switzerland, Cham (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Turnbull, R.: Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets [Preliminary Preprint] (7 2022), https://doi.org/10.48550/arXiv.2207.12218
2022
Cited alongside, same era.
Arsenos, A., Davidhi, A., Kollias, D., Prassopoulos, P., Kollias, S.: Data-driven covid-19 detection through medical imaging. In: 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). pp. 1–5 (2023). https://doi.org/10.1109/ICASSPW59220.2023.10193437
2023
Cited alongside, same era.
Chowdhury, D., Das, A., Dey, A., Banerjee, S., Golec, M., Kollias, D., Kumar, M., Kaur, G., Kaur, R., Arya, R.C., et al.: Covidetector: A transfer learning-based semi supervised approach to detect covid-19 using cxr images. BenchCouncil Transactions on Benchmarks, Standards and Evaluations 3
2023
Cited alongside, same era.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Dollár, P., Girshick, R.B.: Segment anything. 2023 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 3992–4003 (2023), https://api.semanticscholar.org/CorpusID:257952310
2023
Cited alongside, same era.
Salpea, N., Tzouveli, P., Kollias, D.: Medical image segmentation: A review of modern architectures. In: Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VII. pp. 691–708. Springer (2023)
2023
Later among the works it cites.
Arsenos, A., Karampinis, V., Petrongonas, E., Skliros, C., Kollias, D., Kollias, S., Voulodimos, A.: Common corruptions for evaluating and enhancing robustness in air-to-air visual object detection. IEEE Robotics and Automation Letters (2024)
2024
Closest in time.
Arsenos, A., Kollias, D., Petrongonas, E., Skliros, C., Kollias, S.: Uncertainty-guided contrastive learning for single source domain generalisation. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 6935–6939. IEEE (2024)
2024
Closest in time.
Arsenos, A., Petrongonas, E., Filippopoulos, O., Skliros, C., Kollias, D., Kollias, S.: Nefeli: A deep-learning detection and tracking pipeline for enhancing autonomy in advanced air mobility. Available at SSRN 4674579 (2024)
2024
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2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Miah, H., Kollias, D., Pedone, G.L., Provan, D., Chen, F.: Can machine learning assist in diagnosis of primary immune thrombocytopenia? a feasibility study. Diagnostics 14
2024
Closest in time.
Morani, K., Ayana, E.K., Kollias, D., Unay, D.: Covid-19 detection from computed tomography images using slice processing techniques and a modified xception classifier. International Journal of Biomedical Imaging 2024
2024
Closest in time.
Morani, K., Ayana, E.K., Kollias, D., Unay, D.: Detecting covid-19 in computed tomography images: A novel approach utilizing segmentation with unet architecture, lung extraction, and cnn classifier. In: Science and Information Conference. pp. 450–465. Springer (2024)
2024
Closest in time.