ILC-Unet++ for Covid-19 Infection Segmentation
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-13324-4_39
Reference19 articles.
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3. Bougourzi, F., Contino, R., Distante, C., Taleb-Ahmed, A.: CNR-IEMN: a deep learning based approach to recognize COVID-19 from CT-SCAN. In: 2021 IEEE International Conference on Autonomous Systems (IEEE ICAS) (2021)
4. Bougourzi, F., Contino, R., Distante, C., Taleb-Ahmed, A.: Recognition of COVID-19 from CT scans using two-stage deep-learning-based approach: CNR-IEMN. Sensors 21(17), 5878 (2021). https://doi.org/10.3390/s21175878. https://www.mdpi.com/1424-8220/21/17/5878
5. Bougourzi, F., Distante, C., Ouafi, A., Dornaika, F., Hadid, A., Taleb-Ahmed, A.: Per-COVID-19: a benchmark dataset for COVID-19 percentage estimation from CT-scans. J. Imaging 7(9), 189 (2021). https://doi.org/10.3390/jimaging7090189. https://www.mdpi.com/2313-433X/7/9/189
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1. Emb-trattunet: a novel edge loss function and transformer-CNN architecture for multi-classes pneumonia infection segmentation in low annotation regimes;Artificial Intelligence Review;2024-03-14
2. COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge;Sensors;2024-02-28
3. Deep-Covid-SEV: an Ensemble 2D and 3D CNN-Based Approach for Covid-19 Severity Prediction from 3D CT-SCANS;2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW);2023-06-04
4. CNR-IEMN-CD and CNR-IEMN-CSD Approaches for Covid-19 Detection and Covid-19 Severity Detection from 3D CT-scans;Lecture Notes in Computer Science;2023
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