Book Chapters

• Ferrante, E. & Echeveste R. Book: Trustworthy AI in Medical Imaging, Chapter: Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applications. Paperback ISBN: 9780443237614. Elsevier (2024). [link]

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Journals

• Mansilla, L., Claucich, E., Echeveste, R., Milone, D.H., & Ferrante, E. Demographically-Informed Prediction Discrepancy Index: Early Warnings of Demographic Biases for Unlabeled Populations. TMLR (2024). [link]

• Ricci Lara, M.A., Echeveste, R., & Ferrante, E. Addressing Fairness in Artificial Intelligence for Medical Imaging. Nature Communications, https://doi.org/10.1038/s41467-022-32186-3 (2022)[link]

• Echeveste, R., Ferrante, E., Milone, H.D, & Samengo, I. Bridging physiological and perceptual views of
  autism by means of sampling-based Bayesian inference. Network Neuroscience, https://doi.org/10.1162/netn_a_00219 (2022) [link]

• Echeveste, R., Aitchison, L., Hennequin, G. et al. Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference. Nature Neuroscience (2020). https://doi.org/10.1038/s41593-020-0671-1 [link]

• Tourigny, D.S., Karim, M.K.A, Echeveste, R., Kotter, M.R.N., & O'Neill, J.S. Energetic substrate availability regulates synchronous activity in an excitatory neural network. PLOS ONE (2019), 14(8): e0220937. doi: 10.1371/journal.pone.0220937.​ [link​]

• Fonseca, M., Vattuone, N., Clavero, F., Echeveste, R., & Samengo, I. The subjective metric of remembered colors: An information-theoretical analysis of the geometry of human chromatic memory. PLOS ONE (2019), 14(1), e0207992. doi:10.1371/journal.pone.0207992 [link]

• Echeveste, R., & Lengyel, M. The redemption of noise: inference with neural populations. (invited commentary on Ma et al. Nature Neuroscience 9:1432-1438, 2006). Trends in Neurosciences  (2018), 41(11), 767-770. doi:10.1016/j.tins.2018.09.003 [link]

• Trapp, P., Echeveste, R., & Gros, C. E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks. Scientific Reports (2018), 8(1), 8939. doi:10.1038/s41598-018-27099-5 [link]

• Echeveste, R., Eckmann, S., & Gros, C. Drifting states and synchronization induced chaos in autonomous networks of excitable neurons. Frontiers in Computational Neuroscience (2016), 10:98. doi: 10.3389/fncom.2016.00098 [link]

• Echeveste, R., Eckmann, S., & Gros, C. The Fisher Information as a Neural Guiding Principle for Independent Component Analysis. Entropy (2015), 17(6), 3838-3856; doi:10.3390/e17063838. [link]

• Echeveste, R., & Gros, C. Two-trace model for spike-timing dependent synaptic plasticity. Neural Computation (2015), 27 (3), 672-698. doi:10.1162/NECO_a_00707 [link]

• Echeveste, R., & Gros, C. Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules. Frontiers in Robotics and AI (2014), 1:1. doi: 10.3389/frobt.2014.00001 [link]

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Proceedings

• Ricci Lara M.A., Mosquera C., Ferrante E., & Echeveste R. Towards unraveling calibration biases in medical image analysis. The MICCAI 2023 Workshop on Fairness of AI in Medical Imaging (2023) [link]

• Gaggion, N., Echeveste, R., Mansilla, L., Milone, D.H., & Ferrante, E. Unsupervised bias discovery in medical image segmentation. The MICCAI 2023 Workshop on Fairness of AI in Medical Imaging (2023) [link]

• Mansilla, L., Echeveste, R., Milone, H.D., & Ferrante, E. Domain Generalization via Gradient Surgery. Proceedings of the IEEE/CVF International Conference on Computer Vision. (2021) [link]

• Echeveste, R., & Gros, C. An objective function for self-limiting neural plasticity rules. ESANN 2015 Proceedings (2015), ISBN 978-287587014-8. [link]

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Preprints

• Ricci Lara M.A., Mosquera C., Ferrante E., & Echeveste R. Towards unraveling calibration biases in medical image analysis (arXiv, 2023) [link]

• Echeveste, R., Hennequin, G., & Lengyel, M. Asymptotic scaling properties of the posterior mean and variance in the Gaussian scale mixture model (arXiv, 2017) [link]

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