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1. Enhanced continental weathering ($\delta ^{7}$Li, $\varepsilon $Nd) during the rise of East African complex polities: an early large-scale anthropogenic forcing?

Author

Mologni, Carlo, Revel, Marie, Bastian, Luc, Bayon, Germain, Bosch, Delphine, Khalidi, Lamya, and Vigier, Nathalie

Subjects
Earth Critical Zone, Chemical weathering, Li isotope, Nd isotope, Nile deep sea fan, Anthropogenic forcing, Aksumite civilization, Geophysics. Cosmic physics, QC801-809, Chemistry, QD1-999, Geology, QE1-996.5
Abstract

Human-induced environmental disturbances during the Holocene have provided support for the Early Anthropogenic Hypothesis (EAH), which proposes that with the advent of agro-pastoralism and associated deforestation, humans have modified CO2 and CH4 concentrations into the atmosphere. However, only limited evidence exists for human driven chemical alteration of the Earth’s Critical Zone (ECZ) in early antiquity. Here, we explore the impact of human activities on both erosion and chemical weathering patterns in the Nile basin during a time interval that includes the rise of the Aksumite Kingdom and Late Antique Egypt (${\sim }$3–${\sim }$1 ka BP). By coupling lithium and neodymium isotopes ($\delta ^7\mathrm{Li}$, $\varepsilon \mathrm{Nd}$) in clay-size fractions of two marine sediment cores from the Nile Deep Sea Fan (NDSF), we reconstruct the variability of sediment provenance and silicate weathering intensity in the Nile basin over the last 9000 years. Our high temporal resolution data show that for the last ${\sim }$3000 years, the Rosetta Nile Deep Sea Fan has been increasingly fed with clays delivered from the Ethiopian basaltic highlands ($\varepsilon \mathrm{Nd}= {\sim }{-}1$), despite the absence of hydrological intensification and major climatic drivers over that region. Concomitantly, the clay Li isotopic composition shifted towards lower ${\delta }^{7}\mathrm{Li}$ values (${\delta }^{7}\mathrm{Li} = 1$ to ${-}$2), yielding unprecedented negative values for at least the last 100,000 years. Combined with other archaeological, paleo-pedological and organic chemistry inferences, the Li–Nd isotope proxy records indicate a link between the intensification of continental weathering and intensified land-use and water management during the Pre-Aksumite (${\sim }$3 to ${\sim }$2 ka BP) and Aksumite (${\sim }$2 to ${\sim }$1 ka BP) periods. Therefore, our results provide direct support to the hypothesis of an early and large scale anthropogenic forcing on continental chemical weathering. A comparison with previously published records for Central Africa, Central Europe and China suggests that the impact of the intensification of early agriculture on the ECZ may have operated at a global scale starting around four thousand years ago.

Published
2022
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2. Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning

Author

Moutakanni, Théo, Bojanowski, Piotr, Chassagnon, Guillaume, Hudelot, Céline, Joulin, Armand, LeCun, Yann, Muckley, Matthew, Oquab, Maxime, Revel, Marie-Pierre, and Vakalopoulou, Maria

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

AI Foundation models are gaining traction in various applications, including medical fields like radiology. However, medical foundation models are often tested on limited tasks, leaving their generalisability and biases unexplored. We present RayDINO, a large visual encoder trained by self-supervision on 873k chest X-rays. We compare RayDINO to previous state-of-the-art models across nine radiology tasks, from classification and dense segmentation to text generation, and provide an in depth analysis of population, age and sex biases of our model. Our findings suggest that self-supervision allows patient-centric AI proving useful in clinical workflows and interpreting X-rays holistically. With RayDINO and small task-specific adapters, we reach state-of-the-art results and improve generalization to unseen populations while mitigating bias, illustrating the true promise of foundation models: versatility and robustness.

Published
2024

4. CT venography for the diagnosis of postpartum venous thromboembolism: a prospective multi-center cohort study

Author

Revel, Marie-Pierre, Chassagnon, Guillaume, Sanchez, Olivier, Ferretti, Gilbert, Millet, Ingrid, Rocher, Laurence, Maitre, Sophie, Lederlin, Mathieu, Ducou-le-pointe, Hubert, Rousset, Pascal, Bennani, Souhail, Zins, Marc, Bruneau, Bertrand, Tissot, Valentin, Alison, Marianne, Canniff, Emma, Siauve, Nathalie, Vandeventer, Stephanie, Le Blanche, Alain F., Planquette, Benjamin, Tsatsaris, Vassilis, and Coste, Joël

Published
2024
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5. Certification of Deep Learning Models for Medical Image Segmentation

Author

Laousy, Othmane, Araujo, Alexandre, Chassagnon, Guillaume, Paragios, Nikos, Revel, Marie-Pierre, and Vakalopoulou, Maria

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

In medical imaging, segmentation models have known a significant improvement in the past decade and are now used daily in clinical practice. However, similar to classification models, segmentation models are affected by adversarial attacks. In a safety-critical field like healthcare, certifying model predictions is of the utmost importance. Randomized smoothing has been introduced lately and provides a framework to certify models and obtain theoretical guarantees. In this paper, we present for the first time a certified segmentation baseline for medical imaging based on randomized smoothing and diffusion models. Our results show that leveraging the power of denoising diffusion probabilistic models helps us overcome the limits of randomized smoothing. We conduct extensive experiments on five public datasets of chest X-rays, skin lesions, and colonoscopies, and empirically show that we are able to maintain high certified Dice scores even for highly perturbed images. Our work represents the first attempt to certify medical image segmentation models, and we aspire for it to set a foundation for future benchmarks in this crucial and largely uncharted area.

Published
2023

6. Towards Better Certified Segmentation via Diffusion Models

Author

Laousy, Othmane, Araujo, Alexandre, Chassagnon, Guillaume, Revel, Marie-Pierre, Garg, Siddharth, Khorrami, Farshad, and Vakalopoulou, Maria

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The robustness of image segmentation has been an important research topic in the past few years as segmentation models have reached production-level accuracy. However, like classification models, segmentation models can be vulnerable to adversarial perturbations, which hinders their use in critical-decision systems like healthcare or autonomous driving. Recently, randomized smoothing has been proposed to certify segmentation predictions by adding Gaussian noise to the input to obtain theoretical guarantees. However, this method exhibits a trade-off between the amount of added noise and the level of certification achieved. In this paper, we address the problem of certifying segmentation prediction using a combination of randomized smoothing and diffusion models. Our experiments show that combining randomized smoothing and diffusion models significantly improves certified robustness, with results indicating a mean improvement of 21 points in accuracy compared to previous state-of-the-art methods on Pascal-Context and Cityscapes public datasets. Our method is independent of the selected segmentation model and does not need any additional specialized training procedure.

Published
2023

11. The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data

Author

Boulogne, Luuk H., Lorenz, Julian, Kienzle, Daniel, Schön, Robin, Ludwig, Katja, Lienhart, Rainer, Jégou, Simon, Li, Guang, Chen, Cong, Wang, Qi, Shi, Derik, Maniparambil, Mayug, Müller, Dominik, Mertes, Silvan, Schröter, Niklas, Hellmann, Fabio, Elia, Miriam, Dirks, Ine, Bossa, Matías Nicolás, Berenguer, Abel Díaz, Mukherjee, Tanmoy, Vandemeulebroucke, Jef, Sahli, Hichem, Deligiannis, Nikos, Gonidakis, Panagiotis, Huynh, Ngoc Dung, Razzak, Imran, Bouadjenek, Reda, Verdicchio, Mario, Borrelli, Pasquale, Aiello, Marco, Meakin, James A., Lemm, Alexander, Russ, Christoph, Ionasec, Razvan, Paragios, Nikos, van Ginneken, Bram, and Revel, Marie-Pierre

Published
2024
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12. Strategies to safely rule out pulmonary embolism in COVID-19 outpatients: a multicenter retrospective study

Author

Chassagnon, Guillaume, El Hajjam, Mostafa, Boussouar, Samia, Revel, Marie-Pierre, Khoury, Ralph, Ghaye, Benoît, Bommart, Sebastien, Lederlin, Mathieu, Tran Ba, Stephane, De Margerie-Mellon, Constance, Fournier, Laure, Cassagnes, Lucie, Ohana, Mickael, Jalaber, Carole, Dournes, Gael, Cazeneuve, Nicolas, Ferretti, Gilbert, Talabard, Pauline, Donciu, Victoria, Canniff, Emma, Debray, Marie-Pierre, Crutzen, Bernard, Charriot, Jeremy, Rabeau, Valentin, Khafagy, Philippe, Chocron, Richard, Leonard Lorant, Ian, Metairy, Loic, Ruez-Lantuejoul, Lea, Beaune, Sébastien, Hausfater, Pierre, Truchot, Jennifer, Khalil, Antoine, Penaloza, Andrea, Affole, Thibaut, Brillet, Pierre-Yves, Roy, Catherine, Pucheux, Julien, Zbili, Jordan, Sanchez, Olivier, and Porcher, Raphael

Published
2023
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14. High-resolution CT phenotypes in pulmonary sarcoidosis: a multinational Delphi consensus study

Author

Anderson, Adam, Morgenthau, Adam, Gaser, Adrian, Vizel, Alexander, Speranskaya, Alexandra, Gerke, Alicia, Goksel, Altinisik, Undurraga, Alvaro, Sharma, Amita, Oh, Andrea, Leung, Ann, Larici, Anna Rita, Prasse, Antje, Mazzei, Antonietta, Morais, Antonio, Bhalla, Ashu, Del Rio, Belen, Jankharia, Bhavin, Elicker, Brett, Pereira, Carlos, Biegelman-Aubry, Catherine, White, Charles, Ravaglia, Claudia, Hsia, Connie, Schaefer-Prokop, Cornelia, Launay, David, Talwar, Deepak, Castillo, Diego, Patel, Divya, Israel-Biet, Dominique, Valeyre, Dominique, Britt, Edward James, Bargagli, Elena, Bendstrup, Elisabeth, Crouser, Elliott, Nossent, Esther, Shmelev, Eugeny, Porquera, Eva Carmona, Bonella, Francesco, Cohen-Aubart, Fleur, Jeny, Florence, Ferrara, Giovanni, Jin, Gong Yong, Robbie, Hasti, Prosch, Helmut, Sumikawa, Hiromitsu, Ling-Pei, Ho, Lee, Ho Yun, Strambu, Irina, Buendia-Roldan, Ivette, Grutters, Jan, Ryu, Jay, Swigris, Jeff, Miedema, Jelle, Goo, Jin Mo, Barnett, Joseph, Verschakelen, Johny, Goldin, Jonathan, Seo, Joon Beom, Enghelmayer, Juan, Mana, Juan, Behr, Juergen, Patterson, Karen, Antoniou, Katerina, Brown, Kevin, Fujimoto, Kiminori, Savale, Laurent, Maier, Lisa, Richeldi, Luca, Neto, Manuel Lessa Ribeiro, Humbert, Marc, Judson, Marc, Veltkamp, Marcel, Wilsher, Margaret, Molina, Maria, Otaola, Maria, Revel, Marie-Pierre, Silva, Mario, Drent, Marjolein, Schiebler, Mark, Wijsenbeek-Lourens, Marlies, Bonifazi, Martina, Remy-Jardin, Martine, Koslow, Matthew, Balter, Meyer, Kreuter, Michael, Thillai, Muhunthan, Hamzeh, Nabeel, Chaudhuri, Nazia, Mogulkoc, Nesrin, Todd, Nevins, Screaton, Nick, Goh, Nicole, Shah, Nirav, Obi, Ogugua, Shlobin, Oksana, Baranova, Olga, Bresser, Paul, Rottoli, Paula, Brillet, Pierre-Yves, Said-Hartley, Qonita, Borie, Raphael, Mostard, Remy, van Zyl-smit, Richard, Kairalla, Ronaldo, Egashira, Ryoko, Boussouar, Samia, Lee, Sang Min, Bhalla, Sanjeev, Tomassetti, Sara, Quadrelli, Silvia, Hart, Simon P, Danoff, Sonye, Rajan, Sujeet, Choi, Sun Mi, Copley, Susan, Kim, Tae Jung, Johkoh, Takeshi, Corte, Tamera, McLoud, Theresa, Wessendorf, Thomas, Alfaro, Tiago, Arai, Toru, Costabel, Ulrich, Vicens-Zygmunt, Vanesa, Poletti, Venerino, Cottin, Vincent, Vucinic, Violeta, Wuyts, Wim, van Es, Wouter, Jeong, Yeon Joo, Inoue, Yoshikazu, Desai, Sujal R, Sivarasan, Nishanth, Johannson, Kerri A, George, Peter M, Culver, Daniel A, Devaraj, Anand, Lynch, David A, Milne, David, Renzoni, Elisabetta, Nunes, Hilario, Sverzellati, Nicola, Spagnolo, Paolo, Baughman, Robert P, Yadav, Ruchi, Piciucchi, Sara, Walsh, Simon L F, Kouranos, Vasileios, and Wells, Athol U

Published
2024
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18. Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment

Author

Laousy, Othmane, Chassagnon, Guillaume, Oyallon, Edouard, Paragios, Nikos, Revel, Marie-Pierre, and Vakalopoulou, Maria

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Sarcopenia is a medical condition characterized by a reduction in muscle mass and function. A quantitative diagnosis technique consists of localizing the CT slice passing through the middle of the third lumbar area (L3) and segmenting muscles at this level. In this paper, we propose a deep reinforcement learning method for accurate localization of the L3 CT slice. Our method trains a reinforcement learning agent by incentivizing it to discover the right position. Specifically, a Deep Q-Network is trained to find the best policy to follow for this problem. Visualizing the training process shows that the agent mimics the scrolling of an experienced radiologist. Extensive experiments against other state-of-the-art deep learning based methods for L3 localization prove the superiority of our technique which performs well even with a limited amount of data and annotations.

Published
2021

19. Exploring Deep Registration Latent Spaces

Author

Estienne, Théo, Vakalopoulou, Maria, Christodoulidis, Stergios, Battistella, Enzo, Henry, Théophraste, Lerousseau, Marvin, Leroy, Amaury, Chassagnon, Guillaume, Revel, Marie-Pierre, Paragios, Nikos, and Deutsch, Eric

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Explainability of deep neural networks is one of the most challenging and interesting problems in the field. In this study, we investigate the topic focusing on the interpretability of deep learning-based registration methods. In particular, with the appropriate model architecture and using a simple linear projection, we decompose the encoding space, generating a new basis, and we empirically show that this basis captures various decomposed anatomically aware geometrical transformations. We perform experiments using two different datasets focusing on lungs and hippocampus MRI. We show that such an approach can decompose the highly convoluted latent spaces of registration pipelines in an orthogonal space with several interesting properties. We hope that this work could shed some light on a better understanding of deep learning-based registration methods., Comment: 13 pages, 5 figures + 3 figures in supplementary materials Accepted to DART 2021 workshop

Published
2021

22. AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia

Author

Chassagnon, Guillaume, Vakalopoulou, Maria, Battistella, Enzo, Christodoulidis, Stergios, Hoang-Thi, Trieu-Nghi, Dangeard, Severine, Deutsch, Eric, Andre, Fabrice, Guillo, Enora, Halm, Nara, Hajj, Stefany El, Bompard, Florian, Neveu, Sophie, Hani, Chahinez, Saab, Ines, Campredon, Alienor, Koulakian, Hasmik, Bennani, Souhail, Freche, Gael, Lombard, Aurelien, Fournier, Laure, Monnier, Hippolyte, Grand, Teodor, Gregory, Jules, Khalil, Antoine, Mahdjoub, Elyas, Brillet, Pierre-Yves, Ba, Stephane Tran, Bousson, Valerie, Revel, Marie-Pierre, and Paragios, Nikos

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Physics - Medical Physics, Quantitative Biology - Quantitative Methods
Abstract

Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia because of its availability and rapidity. The standard of reference for confirming COVID-19 relies on microbiological tests but these tests might not be available in an emergency setting and their results are not immediately available, contrary to CT. In addition to its role for early diagnosis, CT has a prognostic role by allowing visually evaluating the extent of COVID-19 lung abnormalities. The objective of this study is to address prediction of short-term outcomes, especially need for mechanical ventilation. In this multi-centric study, we propose an end-to-end artificial intelligence solution for automatic quantification and prognosis assessment by combining automatic CT delineation of lung disease meeting performance of experts and data-driven identification of biomarkers for its prognosis. AI-driven combination of variables with CT-based biomarkers offers perspectives for optimal patient management given the shortage of intensive care beds and ventilators.

Published
2020

26. Linear and Deformable Image Registration with 3D Convolutional Neural Networks

Author

Christodoulidis, Stergios, Sahasrabudhe, Mihir, Vakalopoulou, Maria, Chassagnon, Guillaume, Revel, Marie-Pierre, Mougiakakou, Stavroula, and Paragios, Nikos

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Image registration and in particular deformable registration methods are pillars of medical imaging. Inspired by the recent advances in deep learning, we propose in this paper, a novel convolutional neural network architecture that couples linear and deformable registration within a unified architecture endowed with near real-time performance. Our framework is modular with respect to the global transformation component, as well as with respect to the similarity function while it guarantees smooth displacement fields. We evaluate the performance of our network on the challenging problem of MRI lung registration, and demonstrate superior performance with respect to state of the art elastic registration methods. The proposed deformation (between inspiration & expiration) was considered within a clinically relevant task of interstitial lung disease (ILD) classification and showed promising results.

Published
2018

31. Rivaroxaban vs Dalteparin in Cancer-Associated Thromboembolism: A Randomized Trial

Author

Meyer, Guy, Mismetti, Patrick, Chatellier, Gilles, Laporte, Silvy, Decousus, Hervé, Mahé, Isabelle, Falvo, Nicolas, Delluc, Aurélien, Bertoletti, Laurent, Laneau, Christine, Dinut, Aurelia, Aegerter, Philippe, Emmerich, Joseph, Girard, Philippe, Messas, Emmanuel, Revel, Marie-Pierre, Charles-Nelson, Anaïs, Acassat, Sandrine, Grange, Claire, Planquette, Benjamin, Sanchez, Olivier, Plaisance, Ludovic, Poénou, Géraldine, Pernod, Gilles, Imbert, Bernard, Zenati, Nora, Couturaud, Francis, Le Moigne, Emmanuelle, Le Mao, Raphael, Hoffmann, Clément, Elias, Antoine, Elias, Marie, Loffroy, Romaric, Jandot, Maud, Sevestre, Marie-Antoinette, Modéliar Rémond, Santhi Samy, Ray, Valérie, Burnod, Alexis, Roy, Pierre-Marie, Moumneh, Thomas, Henni, Samir, Brebion, Nicolas, Timor-David, Miruna, Constans, Joël, Boulon, Carine, Aquilanti, Sandro, Brisot, Dominique, Bura-Rivière, Alessandra, Sevestre, Marie Antoinette, Timar-David, Miruna, and Constans, Joel

Published
2022
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32. 50-year seasonal variability in East African droughts and floods recorded in central Afar lake sediments (Ethiopia) and their connections with the El Niño–Southern Oscillation.

Author

Mologni, Carlo, Revel, Marie, Chaumillon, Eric, Malet, Emmanuel, Coulombier, Thibault, Sabatier, Pierre, Brigode, Pierre, Hervé, Gwenael, Develle, Anne-Lise, Schenini, Laure, Messous, Medhi, Davtian, Gourguen, Carré, Alain, Bosch, Delphine, Volto, Natacha, Ménard, Clément, Khalidi, Lamya, and Arnaud, Fabien

Subjects
EL Nino, OCEAN temperature, PRECIPITATION variability, ALLUVIAL plains, WATER supply, WATERSHEDS
Abstract

Understanding past and present hydrosystem feedbacks to global ocean–atmospheric interactions represents one of the main challenges to preventing droughts, extreme events, and related human catastrophes in the face of global warming, especially in arid and semiarid environments. In eastern Africa, the El Niño–Southern Oscillation (ENSO) was identified as one of the primary drivers of precipitation variability affecting water availability. However, the northern East African Rift System (EARS) still suffers from the underrepresentation of predictive and ENSO teleconnection models because of the scarcity of local to regional historical or palaeo-data. In this paper, we provide a 50-year seasonal flood and drought chronicle of the Awash River catchment from the study of laminated sediment from Gemeri and Afambo lakes (central Afar region, Ethiopia) with the aim of reconstructing the magnitude of regional hydroclimatic events. Pluricentimetric micro-laminated lithogenic facies alternating with plurimillimetric carbonate-enriched facies are investigated in both lakes. We couple dating methods including radiocarbon, short-lived radionuclides, palaeomagnetic field variations, and varve counting on both lake deposits to build a high-resolution age model and to discuss the regional hydrosedimentary dynamics of the Awash River over the last ∼ 700 years with a focus on the last 50 years. Using a multiproxy approach, we observe that following a multicentennial enhanced hydrological period, the two lakes have experienced a gradual decrease in river load inflow since 1979 CE, attaining extreme drought and high evaporative conditions between 1991 and 1997 CE. In 2014, the construction of a dam and increased agricultural water management in the lower Awash River plain impacted the erodibility of local soils and the hydrosedimentary balance of the lake basins, as evidenced by a disproportionate sediment accumulation rate. Comparison of our quantitative reconstruction with (i) lake water surface evolution, (ii) the interannual Awash River flow rates, and (iii) the El Niño 3.4 model highlights the intermittent connections between ENSO sea surface temperature anomalies, regional droughts, and hydrological conditions in the northern EARS. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Supplementary material to "Fifty-year seasonal variability of East African droughts and floods recorded in Central Afar lake sediments (Ethiopia) and their connections with ENSO"

Author

Mologni, Carlo, primary, Revel, Marie, additional, Chaumillon, Eric, additional, Malet, Emmanuel, additional, Coulombier, Thibault, additional, Sabatier, Pierre, additional, Brigode, Pierre, additional, Gwenael, Hervé, additional, Develle, Anne-Lise, additional, Schenini, Laure, additional, Messous, Medhi, additional, Davtian, Gourguen, additional, Carré, Alain, additional, Bosch, Delphine, additional, Volto, Natacha, additional, Ménard, Clément, additional, Khalidi, Lamya, additional, and Arnaud, Fabien, additional

Published
2024
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34. Fifty-year seasonal variability of East African droughts and floods recorded in Central Afar lake sediments (Ethiopia) and their connections with ENSO

Author

Mologni, Carlo, primary, Revel, Marie, additional, Chaumillon, Eric, additional, Malet, Emmanuel, additional, Coulombier, Thibault, additional, Sabatier, Pierre, additional, Brigode, Pierre, additional, Gwenael, Hervé, additional, Develle, Anne-Lise, additional, Schenini, Laure, additional, Messous, Medhi, additional, Davtian, Gourguen, additional, Carré, Alain, additional, Bosch, Delphine, additional, Volto, Natacha, additional, Ménard, Clément, additional, Khalidi, Lamya, additional, and Arnaud, Fabien, additional

Published
2024
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35. Reversal of cylindrical bronchial dilatations in a subset of adults with cystic fibrosis treated with elexacaftor/tezacaftor/ivacaftor

Author

Cazier, Paul, primary, Chassagnon, Guillaume, additional, Dhote, Théo, additional, Da Silva, Jennifer, additional, Kanaan, Reem, additional, Honoré, Isabelle, additional, Carlier, Nicolas, additional, Revel, Marie-Pierre, additional, Canniff, Emma, additional, Martin, Clémence, additional, and Burgel, Pierre-Régis, additional

Published
2024
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43. 9000 years of human lakeside adaptation in the Ethiopian Afar: Fisher-foragers and the first pastoralists in the Lake Abhe basin during the African Humid Period

Author

Khalidi, Lamya, Mologni, Carlo, Ménard, Clément, Coudert, Lucie, Gabriele, Marzia, Davtian, Gourguen, Cauliez, Jessie, Lesur, Joséphine, Bruxelles, Laurent, Chesnaux, Lorène, Redae, Blade Engda, Hainsworth, Emily, Doubre, Cécile, Revel, Marie, Schuster, Mathieu, and Zazzo, Antoine

Published
2020
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45. Distribution of Mediastinal Lesions Across Multi-Institutional, International, Radiology Databases

Author

Roden, Anja C., Fang, Wentao, Shen, Yan, Carter, Brett W., White, Darin B., Jenkins, Sarah M., Spears, Grant M., Molina, Julian R., Klang, Eyal, Segni, Mattia D., Ackman, Jeanne B., Sanchez, Edward Z., Girard, Nicolas, Shumeri, Engjellush, Revel, Marie-Pierre, Chassagnon, Guillaume, Rubinowitz, Ami, Dicks, Demetrius, Detterbeck, Frank, Ko, Jane P., Falkson, Conrad B., Sigurdson, Samantha, Segreto, Sabrina, Del Vecchio, Silvana, Palmieri, Giovanella, Ottaviano, Margaret, Marino, Mirella, Korst, Robert, and Marom, Edith M.

Published
2020
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47. Willingness to participate in combination screening for lung cancer, chronic obstructive pulmonary disease and cardiovascular disease in four European countries

Author

Behr, Carina, primary, Koffijberg, Hendrik, additional, IJzerman, Maarten, additional, Kauczor, Hans-Ulrich, additional, Revel, Marie-Pierre, additional, Silva, Mario, additional, von Stackelberg, Oyunbileg, additional, van Til, Janine, additional, and Vliegenthart, Rozemarijn, additional

Published
2023
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48. Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs

Author

Bennani, Souhail, primary, Regnard, Nor-Eddine, additional, Ventre, Jeanne, additional, Lassalle, Louis, additional, Nguyen, Toan, additional, Ducarouge, Alexis, additional, Dargent, Lucas, additional, Guillo, Enora, additional, Gouhier, Elodie, additional, Zaimi, Sophie-Hélène, additional, Canniff, Emma, additional, Malandrin, Cécile, additional, Khafagy, Philippe, additional, Koulakian, Hasmik, additional, Revel, Marie-Pierre, additional, and Chassagnon, Guillaume, additional

Published
2023
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