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Fast Fully Automatic Segmentation of the Severely Abnormal Human Right Ventricle from Cardiovascular Magnetic Resonance Images Using a Multi-Scale 3D Convolutional Neural Network.

A. Giannakidis, K. Kamnitsas, V. Spadotto, J. Keegan, G. Smith, B. Glocker, D. Rueckert, S. Ernst, M. Gatzoulis, D. Pennell, S. Babu-Narayan, and D. Firmin. SITIS, page 42-46. IEEE Computer Society, (2016)

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Full Tomographic Reconstruction of 2D Vector Fields using Discrete Integral Data.M. Petrou, and A. Giannakidis. Comput. J., 54 (9): 1491-1504 (2011)Removing Unwanted Text from Architectural Images with Multi-Scale Deformable Attention-Based Machine Learning.M. Lystbæk, A. Giannakidis, M. Beliatis, and M. Olsen. IST, page 1-5. IEEE, (2023)Sampling Bounds for 2-D Vector Field Tomography.A. Giannakidis, and M. Petrou. J. Math. Imaging Vis., 37 (2): 151-165 (2010)Right Ventricular Volume Prediction by Feature Tokenizer Transformer-Based Regression of 2D Echocardiography Small-Scale Tabular Data.T. Bohoran, P. Kampaktsis, L. McLaughlin, J. Leb, S. Moustakidis, G. McCann, and A. Giannakidis. FIMH, volume 13958 of Lecture Notes in Computer Science, page 292-300. Springer, (2023)Automated Recognition of healthy Anterior Cruciate Ligament in Sagittal MR images using Lightweight Deep Learning.A. Siouras, S. Moustakidis, A. Giannakidis, G. Chalatsis, K. Malizos, M. Hantes, S. Tasoulis, and D. Tsaopoulos. IISA, page 1-8. IEEE, (2022)Fast-tracking the Deep Residual Network Training for Arrhythmia Classification by Leveraging the Power of Dynamical Systems.T. Bohoran, P. Kampaktsis, G. McCann, and A. Giannakidis. SITIS, page 184-188. IEEE, (2023)Embracing Uncertainty Flexibility: Harnessing a Supervised Tree Kernel to Empower Ensemble Modelling for 2D Echocardiography-Based Prediction of Right Ventricular Volume.T. Bohoran, P. Kampaktsis, L. McLaughlin, J. Leb, G. McCann, and A. Giannakidis. CoRR, (2024)Fast Fully Automatic Segmentation of the Severely Abnormal Human Right Ventricle from Cardiovascular Magnetic Resonance Images Using a Multi-Scale 3D Convolutional Neural Network.A. Giannakidis, K. Kamnitsas, V. Spadotto, J. Keegan, G. Smith, B. Glocker, D. Rueckert, S. Ernst, M. Gatzoulis, D. Pennell and 2 other author(s). SITIS, page 42-46. IEEE Computer Society, (2016)

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