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Documents dont l'auteur est "Perone, Christian S."

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Nombre de documents: 8

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AskariHemmat, M.H., Honari, S., Rouhier, L., Perone, C. S., Cohen-Adad, J., Savaria, Y., & David, J. P. (octobre 2019). U-net fixed-point quantization for medical image segmentation [Communication écrite]. 1st International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (HAL-MICCAI 2019), Shenzhen, China. Lien externe

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Calabrese, E., Adil, S. M., Cofer, G., Perone, C. S., Cohen-Adad, J., Lad, S. P., & Johnson, G. A. (2018). Postmortem diffusion MRI of the entire human spinal cord at microscopic resolution. NeuroImage: Clinical, 18, 963-971. Disponible

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Nami, H., Perone, C. S., & Cohen-Adad, J. (2022). Histology-informed automatic parcellation of white matter tracts in the rat spinal cord. Frontiers in Neuroanatomy, 16, 960475 (12 pages). Lien externe

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Paugam, F., Lefeuvre, J., Perone, C. S., Gros, C., Reich, D. S., Sati, P., & Cohen-Adad, J. (2019). Open-source pipeline for multi-class segmentation of the spinal cord with deep learning. Magnetic Resonance Imaging, 64, 21-27. Lien externe

Perone, C. S., Ballester, P., Barros, R. C., & Cohen-Adad, J. (2019). Unsupervised domain adaptation for medical imaging segmentation with self-ensembling. NeuroImage, 194, 1-11. Lien externe

Perone, C. S., Calabrese, E., & Cohen-Adad, J. (2018). Spinal cord gray matter segmentation using deep dilated convolutions. Scientific Reports, 8(1), 5966 (13 pages). Disponible

Perone, C. S., & Cohen-Adad, J. (septembre 2018). Deep semi-supervised segmentation with weight-averaged consistency targets [Communication écrite]. 4th International Workshop on Deep Learning in Medical Image Analysis (DLMIA 2018) and 8th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS 2018), Granada, Spain. Lien externe

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Zaimi, A., Wabartha, M., Herman, V., Antonsanti, P.-L., Perone, C. S., & Cohen-Adad, J. (2018). AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks. Scientific Reports, 8(1), 3816 (11 pages). Disponible

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