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Efficient machine learning framework for computer-aided detection of cerebral microbleeds using the Radon transform.

A. Fazlollahi, F. Mériaudeau, V. Villemagne, C. Rowe, P. Yates, O. Salvado, and P. Bourgeat. ISBI, page 113-116. IEEE, (2014)

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Amir Laadhar University of Stuttgart

Other publications of authors with the same name

Automatic detection of small spherical lesions using multiscale approach in 3D medical images.A. Fazlollahi, F. Mériaudeau, V. Villemagne, C. Rowe, P. Desmond, P. Yates, O. Salvado, and P. Bourgeat. ICIP, page 1158-1162. IEEE, (2013)Computer-aided detection of cerebral microbleeds in susceptibility-weighted imaging.A. Fazlollahi, F. Mériaudeau, L. Giancardo, V. Villemagne, C. Rowe, P. Yates, O. Salvado, and P. Bourgeat. Comp. Med. Imag. and Graph., (2015)Automatic Brain Tumour Segmentation in 18F-FDOPA PET Using PET/MRI Fusion.A. Fazlollahi, N. Dowson, F. Mériaudeau, S. Rose, M. Fay, P. Thomas, Z. Taylor, Y. Gal, A. Coulthard, C. Winter and 4 other author(s). DICTA, page 325-329. IEEE Computer Society, (2011)Efficient machine learning framework for computer-aided detection of cerebral microbleeds using the Radon transform.A. Fazlollahi, F. Mériaudeau, V. Villemagne, C. Rowe, P. Yates, O. Salvado, and P. Bourgeat. ISBI, page 113-116. IEEE, (2014)A normalisation framework for quantitative brain imaging; application to quantitative susceptibility mapping.A. Fazlollahi, S. Ayton, P. Bourgeat, P. Raniga, A. Ng, J. Fripp, D. Ames, C. Masters, C. Rowe, V. Villemagne and 2 other author(s). ISBI, page 97-100. IEEE, (2017)Uninformative Frame Detection in Colonoscopy Through Motion, Edge and Color Features.M. Armin, G. Chetty, J. Fripp, H. de Visser, C. Dumas, A. Fazlollahi, F. Grimpen, and O. Salvado. CARE@MICCAI, volume 9515 of Lecture Notes in Computer Science, page 153-162. Springer, (2015)Data Augmentation Using Synthetic Lesions Improves Machine Learning Detection of Microbleeds from MRI.S. Momeni, A. Fazlollahi, P. Bourgeat, P. Raniga, P. Yates, N. Yassi, P. Desmond, J. Fripp, Y. Gao, and O. Salvado. SASHIMI@MICCAI, volume 11037 of Lecture Notes in Computer Science, page 12-19. Springer, (2018)G.fast for FTTdp: Enabling gigabit copper access.D. Wei, A. Fazlollahi, G. Long, and E. Wang. GLOBECOM Workshops, page 668-673. IEEE, (2014)Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text.A. Pampouchidou, O. Simantiraki, A. Fazlollahi, M. Pediaditis, D. Manousos, A. Roniotis, G. Giannakakis, F. Mériaudeau, P. Simos, K. Marias and 2 other author(s). AVEC@ACM Multimedia, page 27-34. ACM, (2016)Reproducibility of multiphase pseudo-continuous arterial spin labeling and the effect of post-processing analysis methods.A. Fazlollahi, P. Bourgeat, X. Liang, F. Mériaudeau, A. Connelly, O. Salvado, and F. Calamante. NeuroImage, (2015)

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Disambiguation of "Fazlollahi, Amir"

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Efficient machine learning framework for computer-aided detection of cerebral microbleeds using the Radon transform.

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Amir Laadhar University of Stuttgart

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Efficient machine learning framework for computer-aided detection of cerebral microbleeds using the Radon transform.

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Amir Laadhar University of Stuttgart

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Efficient machine learning framework for computer-aided detection of cerebral microbleeds using the Radon transform.