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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
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). External link
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. External link
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. External link
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). Available
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. Available
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). Available
Askari Hemmat, M., Savaria, Y., David, J. P., Honari, S., Perone, C. S., Rouhier, L., & Cohen-Adad, J. (2019, October). U-Net Fixed-Point Quantization for Medical Image Segmentation [Paper]. 22nd International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI 2019), Shenzhen, China. External link
Askari Hemmat, M., Honari, S., Rouhier, L., Perone, C. S., Cohen-Adad, J., Savaria, Y., & David, J. P. (2019, October). U-net fixed-point quantization for medical image segmentation [Paper]. 1st International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (HAL-MICCAI 2019), Shenzhen, China. External link
Perone, C. S., & Cohen-Adad, J. (2018, September). Deep semi-supervised segmentation with weight-averaged consistency targets [Paper]. 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. External link
