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831 results on '"Salvo, Francesco"'

1. Unsupervised Feature Orthogonalization for Learning Distortion-Invariant Representations

Author

Doerrich, Sebastian, Di Salvo, Francesco, and Ledig, Christian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

This study introduces unORANIC+, a novel method that integrates unsupervised feature orthogonalization with the ability of a Vision Transformer to capture both local and global relationships for improved robustness and generalizability. The streamlined architecture of unORANIC+ effectively separates anatomical and image-specific attributes, resulting in robust and unbiased latent representations that allow the model to demonstrate excellent performance across various medical image analysis tasks and diverse datasets. Extensive experimentation demonstrates unORANIC+'s reconstruction proficiency, corruption resilience, as well as capability to revise existing image distortions. Additionally, the model exhibits notable aptitude in downstream tasks such as disease classification and corruption detection. We confirm its adaptability to diverse datasets of varying image sources and sample sizes which positions the method as a promising algorithm for advanced medical image analysis, particularly in resource-constrained environments lacking large, tailored datasets. The source code is available at https://github.com/sdoerrich97/unoranic-plus ., Comment: Accepted at RROW@BMVC 2024 (Workshop on Robust Recognition in the Open World at the British Machine Vision Conference)

Published
2024

2. An Embedding is Worth a Thousand Noisy Labels

Author

Di Salvo, Francesco, Doerrich, Sebastian, Rieger, Ines, and Ledig, Christian

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The performance of deep neural networks scales with dataset size and label quality, rendering the efficient mitigation of low-quality data annotations crucial for building robust and cost-effective systems. Existing strategies to address label noise exhibit severe limitations due to computational complexity and application dependency. In this work, we propose WANN, a Weighted Adaptive Nearest Neighbor approach that builds on self-supervised feature representations obtained from foundation models. To guide the weighted voting scheme, we introduce a reliability score, which measures the likelihood of a data label being correct. WANN outperforms reference methods, including a linear layer trained with robust loss functions, on diverse datasets of varying size and under various noise types and severities. WANN also exhibits superior generalization on imbalanced data compared to both Adaptive-NNs (ANN) and fixed k-NNs. Furthermore, the proposed weighting scheme enhances supervised dimensionality reduction under noisy labels. This yields a significant boost in classification performance with 10x and 100x smaller image embeddings, minimizing latency and storage requirements. Our approach, emphasizing efficiency and explainability, emerges as a simple, robust solution to overcome inherent limitations of deep neural network training. The code is available at https://github.com/francescodisalvo05/wann-noisy-labels ., Comment: Preprint - Under Review

Published
2024

3. Privacy-preserving datasets by capturing feature distributions with Conditional VAEs

Author

Di Salvo, Francesco, Tafler, David, Doerrich, Sebastian, and Ledig, Christian

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Large and well-annotated datasets are essential for advancing deep learning applications, however often costly or impossible to obtain by a single entity. In many areas, including the medical domain, approaches relying on data sharing have become critical to address those challenges. While effective in increasing dataset size and diversity, data sharing raises significant privacy concerns. Commonly employed anonymization methods based on the k-anonymity paradigm often fail to preserve data diversity, affecting model robustness. This work introduces a novel approach using Conditional Variational Autoencoders (CVAEs) trained on feature vectors extracted from large pre-trained vision foundation models. Foundation models effectively detect and represent complex patterns across diverse domains, allowing the CVAE to faithfully capture the embedding space of a given data distribution to generate (sample) a diverse, privacy-respecting, and potentially unbounded set of synthetic feature vectors. Our method notably outperforms traditional approaches in both medical and natural image domains, exhibiting greater dataset diversity and higher robustness against perturbations while preserving sample privacy. These results underscore the potential of generative models to significantly impact deep learning applications in data-scarce and privacy-sensitive environments. The source code is available at https://github.com/francescodisalvo05/cvae-anonymization ., Comment: Accepted at BMVC 2024

Published
2024

4. Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization

Author

Doerrich, Sebastian, Di Salvo, Francesco, and Ledig, Christian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Despite notable advancements, the integration of deep learning (DL) techniques into impactful clinical applications, particularly in the realm of digital histopathology, has been hindered by challenges associated with achieving robust generalization across diverse imaging domains and characteristics. Traditional mitigation strategies in this field such as data augmentation and stain color normalization have proven insufficient in addressing this limitation, necessitating the exploration of alternative methodologies. To this end, we propose a novel generative method for domain generalization in histopathology images. Our method employs a generative, self-supervised Vision Transformer to dynamically extract characteristics of image patches and seamlessly infuse them into the original images, thereby creating novel, synthetic images with diverse attributes. By enriching the dataset with such synthesized images, we aim to enhance its holistic nature, facilitating improved generalization of DL models to unseen domains. Extensive experiments conducted on two distinct histopathology datasets demonstrate the effectiveness of our proposed approach, outperforming the state of the art substantially, on the Camelyon17-wilds challenge dataset (+2%) and on a second epithelium-stroma dataset (+26%). Furthermore, we emphasize our method's ability to readily scale with increasingly available unlabeled data samples and more complex, higher parametric architectures. Source code is available at https://github.com/sdoerrich97/vits-are-generative-models ., Comment: Accepted at MICCAI 2024. This is the submitted manuscript with added link to github repo and funding acknowledgements. No further post submission improvements or corrections were integrated. Final version not published yet

Published
2024

7. MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

Author

Di Salvo, Francesco, Doerrich, Sebastian, and Ledig, Christian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The integration of neural-network-based systems into clinical practice is limited by challenges related to domain generalization and robustness. The computer vision community established benchmarks such as ImageNet-C as a fundamental prerequisite to measure progress towards those challenges. Similar datasets are largely absent in the medical imaging community which lacks a comprehensive benchmark that spans across imaging modalities and applications. To address this gap, we create and open-source MedMNIST-C, a benchmark dataset based on the MedMNIST+ collection covering 12 datasets and 9 imaging modalities. We simulate task and modality-specific image corruptions of varying severity to comprehensively evaluate the robustness of established algorithms against real-world artifacts and distribution shifts. We further provide quantitative evidence that our simple-to-use artificial corruptions allow for highly performant, lightweight data augmentation to enhance model robustness. Unlike traditional, generic augmentation strategies, our approach leverages domain knowledge, exhibiting significantly higher robustness when compared to widely adopted methods. By introducing MedMNIST-C and open-sourcing the corresponding library allowing for targeted data augmentations, we contribute to the development of increasingly robust methods tailored to the challenges of medical imaging. The code is available at https://github.com/francescodisalvo05/medmnistc-api ., Comment: Accepted at MICCAI Workshop on Advancing Data Solutions in Medical Imaging AI (ADSMI @ MICCAI 2024)

Published
2024

8. Rethinking Model Prototyping through the MedMNIST+ Dataset Collection

Author

Doerrich, Sebastian, Di Salvo, Francesco, Brockmann, Julius, and Ledig, Christian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The integration of deep learning based systems in clinical practice is often impeded by challenges rooted in limited and heterogeneous medical datasets. In addition, prioritization of marginal performance improvements on a few, narrowly scoped benchmarks over clinical applicability has slowed down meaningful algorithmic progress. This trend often results in excessive fine-tuning of existing methods to achieve state-of-the-art performance on selected datasets rather than fostering clinically relevant innovations. In response, this work presents a comprehensive benchmark for the MedMNIST+ database to diversify the evaluation landscape and conduct a thorough analysis of common convolutional neural networks (CNNs) and Transformer-based architectures, for medical image classification. Our evaluation encompasses various medical datasets, training methodologies, and input resolutions, aiming to reassess the strengths and limitations of widely used model variants. Our findings suggest that computationally efficient training schemes and modern foundation models hold promise in bridging the gap between expensive end-to-end training and more resource-refined approaches. Additionally, contrary to prevailing assumptions, we observe that higher resolutions may not consistently improve performance beyond a certain threshold, advocating for the use of lower resolutions, particularly in prototyping stages, to expedite processing. Notably, our analysis reaffirms the competitiveness of convolutional models compared to ViT-based architectures emphasizing the importance of comprehending the intrinsic capabilities of different model architectures. Moreover, we hope that our standardized evaluation framework will help enhance transparency, reproducibility, and comparability on the MedMNIST+ dataset collection as well as future research within the field. Code is available at https://github.com/sdoerrich97 .

Published
2024

9. Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification

Author

Doerrich, Sebastian, Archut, Tobias, Di Salvo, Francesco, and Ledig, Christian

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Traditional deep learning models implicity encode knowledge limiting their transparency and ability to adapt to data changes. Yet, this adaptability is vital for addressing user data privacy concerns. We address this limitation by storing embeddings of the underlying training data independently of the model weights, enabling dynamic data modifications without retraining. Specifically, our approach integrates the $k$-Nearest Neighbor ($k$-NN) classifier with a vision-based foundation model, pre-trained self-supervised on natural images, enhancing interpretability and adaptability. We share open-source implementations of a previously unpublished baseline method as well as our performance-improving contributions. Quantitative experiments confirm improved classification across established benchmark datasets and the method's applicability to distinct medical image classification tasks. Additionally, we assess the method's robustness in continual learning and data removal scenarios. The approach exhibits great promise for bridging the gap between foundation models' performance and challenges tied to data privacy. The source code is available at https://github.com/TobArc/privacy-aware-image-classification-with-kNN., Comment: Accepted at 21st IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2024)

Published
2024

10. Chapter Evolving Roles of Spontaneous Reporting Systems to Assess and Monitor Drug Safety

Author

Cosimo Antonazzo, Ippazio, Salvo, Francesco, Raschi, Emanuel, Pariente, Antoine, Moretti, Ugo, Poluzzi, Elisabetta, and De Ponti, Fabrizio

Subjects
pharmacovigilance, signal, spontaneous reporting system, disproportionality analysis, Public health and preventive medicine
Abstract

This chapter aims to describe current and emerging roles of spontaneous reporting systems (SRSs) for assessing and monitoring drug safety. Moreover, it offers a perspective on the near future, which entails the so-called era of Big Data, keeping in mind both regulator and researcher viewpoints. After a panorama on key data sources and analyses of post-marketing data of adverse drug reactions, a critical appraisal of methodological issues and debated future applications of SRSs will be presented, including the exploitation and challenges in evidence integration (i.e., merging and combining heterogeneous sources of data into a unique indicator of risk) and patient’s reporting via social media. Finally, a call for a responsible use of these studies is offered, with a proposal on a set of minimum requirements to assess the quality of disproportionality analysis in terms of study conception, performing and reporting.

Published
2021
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13. unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features

Author

Doerrich, Sebastian, Di Salvo, Francesco, and Ledig, Christian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We introduce unORANIC, an unsupervised approach that uses an adapted loss function to drive the orthogonalization of anatomy and image-characteristic features. The method is versatile for diverse modalities and tasks, as it does not require domain knowledge, paired data samples, or labels. During test time unORANIC is applied to potentially corrupted images, orthogonalizing their anatomy and characteristic components, to subsequently reconstruct corruption-free images, showing their domain-invariant anatomy only. This feature orthogonalization further improves generalization and robustness against corruptions. We confirm this qualitatively and quantitatively on 5 distinct datasets by assessing unORANIC's classification accuracy, corruption detection and revision capabilities. Our approach shows promise for enhancing the generalizability and robustness of practical applications in medical image analysis. The source code is available at https://github.com/sdoerrich97/unORANIC., Comment: to be published in International Workshop on Machine Learning in Medical Imaging (MLMI 2023)

Published
2023

15. The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration

Author

Fusaroli, Michele, Salvo, Francesco, Begaud, Bernard, AlShammari, Thamir M., Bate, Andrew, Battini, Vera, Brueckner, Andreas, Candore, Gianmario, Carnovale, Carla, Crisafulli, Salvatore, Cutroneo, Paola Maria, Dolladille, Charles, Drici, Milou-Daniel, Faillie, Jean-Luc, Goldman, Adam, Hauben, Manfred, Herdeiro, Maria Teresa, Mahaux, Olivia, Manlik, Katrin, Montastruc, François, Noguchi, Yoshihiro, Norén, G. Niklas, Noseda, Roberta, Onakpoya, Igho J., Pariente, Antoine, Poluzzi, Elisabetta, Salem, Myriam, Sartori, Daniele, Trinh, Nhung T. H., Tuccori, Marco, van Hunsel, Florence, van Puijenbroek, Eugène, Raschi, Emanuel, and Khouri, Charles

Published
2024
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16. The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement

Author

Fusaroli, Michele, Salvo, Francesco, Begaud, Bernard, AlShammari, Thamir M., Bate, Andrew, Battini, Vera, Brueckner, Andreas, Candore, Gianmario, Carnovale, Carla, Crisafulli, Salvatore, Cutroneo, Paola Maria, Dolladille, Charles, Drici, Milou-Daniel, Faillie, Jean-Luc, Goldman, Adam, Hauben, Manfred, Herdeiro, Maria Teresa, Mahaux, Olivia, Manlik, Katrin, Montastruc, François, Noguchi, Yoshihiro, Norén, G. Niklas, Noseda, Roberta, Onakpoya, Igho J., Pariente, Antoine, Poluzzi, Elisabetta, Salem, Myriam, Sartori, Daniele, Trinh, Nhung T. H., Tuccori, Marco, van Hunsel, Florence, van Puijenbroek, Eugène, Raschi, Emanuel, and Khouri, Charles

Published
2024
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17. Advanced Sensors-Based Infrastructure for Active Ageing and Well-Being: The 4Frailty Project

Author

Andò, Bruno, Manenti, Mattia, Corvisieri, Giacomo, Fici, Tullio Flavio, Spampinato, Antonio G., Di Salvo, Francesco, Ragusa, Emanuele, Zampognaro, Paolo, Falanga, Vincenzo, Saccà, Federica, Lovell, Nigel H., Advisory Editor, Oneto, Luca, Advisory Editor, Piotto, Stefano, Advisory Editor, Rossi, Federico, Advisory Editor, Samsonovich, Alexei V., Advisory Editor, Babiloni, Fabio, Advisory Editor, Liwo, Adam, Advisory Editor, Magjarevic, Ratko, Advisory Editor, Fiorini, Laura, editor, Sorrentino, Alessandra, editor, Siciliano, Pietro, editor, and Cavallo, Filippo, editor

Published
2024
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18. unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features

Author

Doerrich, Sebastian, Di Salvo, Francesco, Ledig, Christian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Xiaohuan, editor, Xu, Xuanang, editor, Rekik, Islem, editor, Cui, Zhiming, editor, and Ouyang, Xi, editor

Published
2024
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23. The enhanced national pharmacovigilance system implemented for COVID-19 vaccines in France: A 2-year experience report

Author

Crommelynck, Samuel, Grandvuillemin, Aurélie, Ferard, Claire, Mounier, Céline, Gault, Nathalie, Pierron, Evelyne, Jacquot, Baptiste, Vaillant, Tiphaine, Chatelet, Isabelle Parent du, Jacquet, Alexis, Salvo, Francesco, Alt, Martine, Bagheri, Haleh, Micallef, Joëlle, Pariente, Antoine, Gautier, Sophie, Valnet-Rabier, Marie-Blanche, Atzenhoffer, Marina, Lepelley, Marion, Cottin, Judith, Lacroix, Isabelle, Gras, Valérie, Massy, Nathalie, Dhanani, Alban, Vella, Philippe, Shaim, Youssef, Baril, Laurence, Jonville-Béra, Annie-Pierre, and Benkebil, Mehdi

Published
2024
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27. Quelle place pour l’automatisation intelligente et l’intelligence artificielle pour préserver et renforcer l’expertise en vigilance devant l’augmentation des déclarations ?

Author

Pariente, Antoine, Micallef, Joëlle, Lahouegue, Amir, Molimard, Mathieu, Auffret, Marine, Chouchana, Laurent, Denis, Bernard, Faillie, Jean Luc, Grandvuillemin, Aurelie, Letinier, Louis, Pierron, Evelyne, Pons, Catherine, Pujade, Iris, Rubino, Heather, and Salvo, Francesco

Published
2023
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29. Hyper inflammatory syndrome following COVID-19 mRNA vaccine in children: A national post-authorization pharmacovigilance study

Author

Donzeau, Aurelie, El Aridi, Layal, Lety, Sophie, Leboucher, Bertrand, Baur, Agnes, Jeusset, Lucas, Selegny, Maelle, Fedorczuk, Cristian, Lajus, Marion, Bensaid, Philippe, Laoudi, Yacine, Pons, Charlotte, Robert, Anne-Cécile, Beaucourt, Camille, De Pontual, Loïc, Richard, Muriel, Goisque, Etienne, Iriart, Xavier, Brissaud, Olivier, Segretin, Pierre, Molimard, Julie, Orecel, Marie-Clothilde, Benoit, Gregoire, Bongiovanni, Lucille, Guerder, Margaux, Pouyau, Robin, De Guillebon De Resnes, Jean-Marie, Mezgueldi, Ellia, Cour-Andlauer, Fleur, Horvat, Come, Poinsot, Pierre, Frachette, Cecile, Ouziel, Antoine, Gillet, Yves, Barrey, Catherine, Brouard, Jacques, Villedieu, Florence, Ro, Vathanaksambath, Elanga, Narcisse, Gajdos, Vincent, Basmaci, Romain, Mutar, Hadile, Rouget, Sébastien, Nattes, Elodie, Hau, Isabelle, Biscardi, Sandra, El Jurdi, Houmam, Jung, Camille, Semama, Denis, Huet, Frederic, Zoccarato, Anne-Marie, Sarakbi, Mayssa, Mortamet, Guillaume, Bost-Bru, Cécile, Bassil, Joachim, Vinit, Caroline, Hentgen, Véronique, Leroux, Pascal, Bertrand, Valérie, Parrod, Caroline, Craiu, Irina, Kone-Paut, Isabelle, Durand, Philippe, Tissiere, Pierre, Claude, Caroline, Morelle, Guillaume, Guiddir, Tamazoust, Borocco, Charlotte, Delion, Frédérique, Guillot, Camille, Leteurtre, Stéphane, Dubos, François, Jouancastay, Mylene, Martinot, Alain, Voeusler, Valentine, Languepin, Jeanne, Garrec, Nathalie, Demersay, Arnaud Chalvon, Morand, Aurélie, Bosdure, Emmanuelle, Vanel, Noémie, Ughetto, Fabrice, Michel, Fabrice, Caujolle, Marie, Blonde, Renaud, Nguyen, Jacqueline, Vignaud, Olivier, Masserot-Lureau, Caroline, Gouraud, François, Araujo, Carine, Ingrao, Tara, Naji, Sanaa, Sehaba, Mohammed, Roche, Christine, Carbasse, Aurelia, Milesi, Christophe, Mazeghrane, Mustapha, Haupt, Sandrine, Schweitzer, Cyril, Romefort, Benedicte, Launay, Elise, Guen, Christèle Gras-Le, Ali, Ahmed, Blot, Nathalie, Tran, Antoine, Rancurel, Anne, Afanetti, Mickael, Odorico, Sophie, Talmud, Deborah, Chosidow, Anais, Romain, Anne-Sophie, Grimprel, Emmanuel, Pouletty, Marie, Gaschignard, Jean, Corseri, Olivier, Faye, Albert, Melki, Isabelle, Ducrocq, Camille, Benzoïd, Cherine, Lokmer, Johanna, Dauger, Stéphane, Chomton, Maryline, Deho, Anna, Lebourgeois, Fleur, Renolleau, Sylvain, Lesage, Fabrice, Moulin, Florence, Dupic, Laurent, Pinhas, Yael, Debray, Agathe, Chalumeau, Martin, Abadie, Véronique, Frange, Pierre, Cohen, Jeremie F, Allali, Slimane, Curtis, William, Belhadjer, Zahra, Auriau, Johanne, Méot, Mathilde, Houyel, Lucile, Bonnet, Damien, Delacourt, Christophe, Bader Meunier, Brigitte, Quartier, Pierre, Shaim, Youssef, Baril, Laurence, Crommelynck, Samuel, Jacquot, Baptiste, Blanc, Philippe, Maledon, Natacha, Robert, Blandine, Loeile, Camille, Cazau, Clémence, Loron, Gauthier, Gaga, Simona, Vittot, Cécile, El Nabhani, Loubna, Buisson, François, Prudent, Muriel, Flodrops, Hugues, Mokraoui, Fadhila, Escoda, Simon, Deschamps, Nina, Bonnemains, Laurent, Mahi, Sarah-Louisa, Mertes, Clara, Terzic, Joelle, Helms, Julie, Idier, Charlotte, Chenichene, Soraya, Magdolena Ursulescu, Nicoleta, Beaujour, Gladys, Hakim, Abdelhak, Miquel, Alice, Rey, Agnès, Wiedermann, Arnaud, Charbonneau, Anne, Veauvy-Juven, Agnès, Ferry, Alexandrine, Mandelcwajg, Alexis, Rousseau, Alix, Prenant, Amandine, Bourneuf, Anne-Laure, Filleron, Anne, Robine, Audrey, Félix, Arthur, Parizel, Aude, Labarre, Aurélie, Cantais, Aymeric, Ros, Barbara, Coulon, Basile, Biot, Blandine, Dalichoux, Bérengère, Fournier, Benjamin, Cagnard, Benoit, Vanel, Blandine, Brossier, David, Ménager, Bruno, Ozanne, Bruno, Marie-Jeanne, Carole, Bergerot, Camille, Chavy, Camille, Guidon, Camille, Fabre, Candice, Galeotti, Caroline, Baker, Catherine, Ballot-Schmit, Claire, Belleau, Céline, Charasse, Céline, Favel, Caroline, Toumi, Chadia, Ferrandiz, Charlène, Couturier, Charlotte, Pouchoux, Charlotte, Chomton-Cailliez, Maryline, Kevorkian-Verguet, Charlotte, Brunet, Clément, Manteau, Céline, Mougey, Clémence, Santy, Coline, Fitament, Coralie, Petriat, Charlotte, Rebelle, Charlotte, Charron, Cyril, Dartus, Maxime, Toulorge, David, Guillou-Debuisson, Cécile, Bartebin, Dorann, Klein, Valérie, Broustal, E, Desselas, E, Marteau, Elodie, Bouvrot, Emmanuelle, Delacroix, Elise, Coinde, Edeline, Elnabhani, Loubna, Amouyal, Elsa, Chaillou, Emilie, Gabilly-Bernard, Emeline, Ruiz, Emilie, Thibault, Emilie, Robin, Emilie, Darrieux, Etienne, Blondel, Eva, Socchi, Floriane, Cazassus, François, Bajolle, Fanny, Lacin, Fatma, Madhi, Fouad, Zekre, Franck, Guerin, François, Boussicault, Gerald, Ginies, Henri, Magloire, Gnansounou, Arnold, Guilhem, Coulognon, Ines, Sicard-Cras, Iona, Kahn, Jean-Emmanuel, Bordet, Jeanne, Fausser, Jeanne-Lise, Baleine, Jean-François, Brice, Josephine, Gendras, Julie, Pekin, Kaan, Norbert, Karine, Karsenty, Clément, Savary, Léa, Martinat, Laurence, Lesniewski, Léa, Charbonnier, Lorelei, Alexandre, Louise, Percheron, Lucas, Vincenti, Marie, Lanzini, Manon, Grisval, Margot, Mercy, Marianne, Lampin, Marie-Emilie, Desgranges, Marie, Duperril, Marie, Orcel, Marie-Clothilde, Audier, Marion, Favier, Marion, Carpentier, Mathieu, Balcean, Mathilde, Bonnet, Mathilde, Jouret, Maurine, Delattre, Marie, Levy, Michael, Valensi, Michael, Shum, Mickael, Dumortier, Morgane, Gelin, Morgane, Nemmouchi, Morgane, Williaume, Morgane, Sebaha, M, Genetay-Stanescu, Nicoleta, Giroux, Nathan, Crassard, Nicolas, Derridj, Neil, Lachaume, Noemie, Werner, Oscar, Guilluy, Olivier, Richer, Olivier, Tirel, Olivier, Pauvert, Aurianne, Casha, Paul, Perez, Noémie, Gras, Pauline, Leger, Pierre-Louis, Pinchou, Marion, Mornand, Pierre, Largo, Prisca, Ibanez, Ramona-Christina, Roulland, Charlotte, Hadah Albarazi, Salam, Bichali, Said, Faton, Sarah, Schott, Amandine, Walser, Sébastien, Guillaume, Severine, Vincent, Solene, Galene-Gromez, Sophie, Kozisek, Stanislas, Maugard, Thierry, Blanc, Thierry, Navarro, Thierry, Lauvray, Thomas, Kovacs, Tamas, Launay, Valérie, Despert, Véronique, Lhostis, Victoria, Gall, Virginie, Micaelli, Xavier, Benadjaoud, Yasmine, Matoussi, Zied, Géniaux, Hélène, Facile, Anthony, Pietri, Tessa, Palassin, Pascale, Pinel, Sylvine, Chouchana, Laurent, Callot, Delphine, Boulay, Charlène, Ouldali, Naïm, Bagheri, Haleh, Salvo, Francesco, Antona, Denise, Pariente, Antoine, Leblanc, Claire, Tebacher, Martine, Micallef, Joëlle, Levy, Corinne, Cohen, Robert, Javouhey, Etienne, Bader-Meunier, Brigitte, Ovaert, Caroline, Hentgen, Veronique, Guen, Christelle Gras-Le, Angoulvant, François, and Belot, Alexandre

Published
2022
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39. The reporting of disproportionality analysis in pharmacovigilance: spotlight on the READUS-PV guideline.

Author

Fusaroli, Michele, Salvo, Francesco, Khouri, Charles, and Raschi, Emanuel

Subjects
DRUG side effects, MEDICATION safety, SIGNAL detection, REPRODUCIBLE research, PUBLISHED articles
Abstract

Disproportionality analyses are the most-commonly used study design used in the post-marketing phase to detect suspected adverse drug reactions in individual case safety reports. Recent years have witnessed an exponential increase in published articles on disproportionality analyses, thanks to publicly accessible databases. Unfortunately, this trend was accompanied by concerns on lack of transparency and misinterpretation of results, both generating unjustified alarm and diluting true signals into overwhelming noise. The READUS-PV guideline for reporting disproportionality analysis was developed to tackle this emerging issue. In this perspective article, we describe the rationale behind the development of the READUS-PV guideline, the first collaborative initiative to harmonize the reporting of disproportionality analyses. The adoption of the checklists will assist researchers, regulators, and reviewers in the reporting, assessment, and publication of disproportionality analyses. Acknowledging the challenges ahead of effective implementation, we advocate for a global endorsement by Pharmacology Journals. A wide dissemination of the READUS-PV guideline is crucial to foster transparency and reproducibility of pharmacovigilance research, supporting an effective exploitation of disproportionality analysis among other irreplaceable post-marketing research tools to ensure drug safety. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Resective epilepsy surgery and its impact on depression in adults: a systematic review, meta-analysis, and implications for future research.

Author

Poblete, Natalia Hernandez, Gay, Florian, Salvo, Francesco, Micoulaud-Franchi, Jean-Arthur, Bienvenu, Thomas, Coelho, Julien, and Aupy, Jerome

Subjects
TEMPORAL lobectomy, HIPPOCAMPAL sclerosis, HAMILTON Depression Inventory, TEMPORAL lobe epilepsy, MEDICAL personnel, PREOPERATIVE risk factors, EPILEPSY, MIGRAINE aura
Published
2024
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48. Early extubation with immediate non-invasive ventilation versus standard weaning in intubated patients for coronavirus disease 2019: a retrospective multicenter study

Author

Cammarota, Gianmaria, Vaschetto, Rosanna, Azzolina, Danila, De Vita, Nello, Olivieri, Carlo, Ronco, Chiara, Longhini, Federico, Bruni, Andrea, Colombo, Davide, Pissaia, Claudio, Prato, Federico, Maestrone, Carlo, Maestrone, Matteo, Vetrugno, Luigi, Bove, Tiziana, Lemut, Francesco, Taretto, Elisa, Locatelli, Alessandro, Barzaghi, Nicoletta, Cerrano, Martina, Ravera, Enrico, Zanza, Christian, Selva, Andrea Della, Blangetti, Ilaria, Salvo, Francesco, Racca, Fabrizio, Longhitano, Yaroslava, Boscolo, Annalisa, Sguazzotti, Ilaria, Bonato, Valeria, Grossi, Francesca, Crimaldi, Federico, Perucca, Raffaella, Boniolo, Ester, Verdina, Federico, Vignazia, Gian Luca, Santangelo, Erminio, Tarquini, Riccardo, Zanoni, Marta, Messina, Antonio, Marin, Matteo, Bacigalupo, Paola, Sileci, Graziana, Sella, Nicolò, De Robertis, Edardo, Corte, Francesco Della, and Navalesi, Paolo

Published
2021
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