Zobrazeno 31 - 40
of 74
pro vyhledávání: '"Maria Vakalopoulou"'
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2021
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2021
In this article, we present a deep multitask learning framework able to couple semantic segmentation and change detection using fully convolutional long short-term memory (LSTM) networks. In particular, we present a UNet-like architecture (L-UNet) th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9898d1df943fa827905104fe59fac44
https://hal.inria.fr/hal-03140492/file/IEEE_Transactions_on_Geoscience_and_Remote_Sensing.pdf
https://hal.inria.fr/hal-03140492/file/IEEE_Transactions_on_Geoscience_and_Remote_Sensing.pdf
Autor:
Théophraste Henry, Enzo Battistella, Marie-Pierre Revel, Maria Vakalopoulou, Marvin Lerousseau, Amaury Leroy, Nikos Paragios, Guillaume Chassagnon, Théo Estienne, Stergios Christodoulidis, Eric Deutsch
Publikováno v:
Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health
DART in MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention
DART in MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2021, Strasbourg, France. pp.112-122, ⟨10.1007/978-3-030-87722-4_11⟩
Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health ISBN: 9783030877217
DART/FAIR@MICCAI
DART in MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention
DART in MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2021, Strasbourg, France. pp.112-122, ⟨10.1007/978-3-030-87722-4_11⟩
Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health ISBN: 9783030877217
DART/FAIR@MICCAI
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6da65aa35f633dfb33eac7b9bcb348d0
https://hal.science/hal-03524105
https://hal.science/hal-03524105
Autor:
Nikos Paragios, Chahinez Hani, Maxime Barat, Hasmik Koulakian, Stéphane Tran Ba, Eric Deutsch, Trieu Nghi Hoang-Thi, Ahmed Mekki, Téodor Grand, Severine Dangeard, Fabrice Andre, Souhail Bennani, Pierre Yves Brillet, Laure Fournier, A. Lombard, Robert Carlier, Hippolyte Monnier, Jules Gregory, Maria Vakalopoulou, Enzo Battistella, Antoine Khalil, Stefany El Hajj, Marie-Pierre Revel, Elyas Mahdjoub, Sophie Neveu, Stergios Christodoulidis, Nara Halm, Alienor Campredon, Florian Bompard, G. Freche, Valérie Bousson, Enora Guillo, Guillaume Chassagnon, Yann Nguyen, Ines Saab
Publikováno v:
Medical Image Analysis
Medical Image Analysis, 2021, 67, pp.101860. ⟨10.1016/j.media.2020.101860⟩
Medical Image Analysis, Elsevier, 2021, 67, pp.101860. ⟨10.1016/j.media.2020.101860⟩
Medical Image Analysis, 2021, 67, pp.101860. ⟨10.1016/j.media.2020.101860⟩
Medical Image Analysis, Elsevier, 2021, 67, pp.101860. ⟨10.1016/j.media.2020.101860⟩
Highlights • An algorithm for automatic Covid-19 quantification based on 2D & 3D deep convolutional neural networks is presented. • A Covid-19-specific holistic, highly compact multi-omics signature integrating imaging/clinical/ biological data a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5fb000babca11431efdccaed94568cf0
https://doi.org/10.1016/j.media.2020.101860
https://doi.org/10.1016/j.media.2020.101860
Autor:
Maria Vakalopoulou, Eric Deutsch, Enzo Battistella, Nikos Paragios, Théophraste Henry, Charlotte Robert, Marvin Lerousseau, Théo Estienne, Alexandre Carré
Publikováno v:
Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data ISBN: 9783030718268
MICCAI (Challenges)
MICCAI (Challenges)
Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and performing registration strategies. In this short paper, we summarise o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aaac90951c790592fbd28c31c28fc325
https://doi.org/10.1007/978-3-030-71827-5_11
https://doi.org/10.1007/978-3-030-71827-5_11
Autor:
Eric Deutsch, Norbert Bus, Sonia Martinot, Charlotte Robert, Nikos Paragios, Maria Vakalopoulou
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872014
MICCAI (4)
MICCAI (4)
Monte-Carlo simulation of radiotherapy dose remains an extremely time-consuming task, despite being still the most precise tool for radiation transport calculation. To circumvent this issue, deep learning offers promising avenues. In this paper, we e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d6e1966110a69424fda13e1f4b953fc
https://doi.org/10.1007/978-3-030-87202-1_48
https://doi.org/10.1007/978-3-030-87202-1_48
Autor:
Eugenie Maurin, Béatrice Grange, Evangelia I. Zacharaki, Nikos Paragios, Laurent Jallades, Pierre Sujobert, Maria Vakalopoulou, Mihir Sahasrabudhe
We investigate the use of recent advances in deep learning and propose an end-to-end trainable multi-instance convolutional neural network within a mixture-of-experts formulation that combines information from two types of data—images and clinical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6006c06b2512afe1cd48e71491d1ce65
https://hal.archives-ouvertes.fr/hal-03032875/file/hal.pdf
https://hal.archives-ouvertes.fr/hal-03032875/file/hal.pdf
Autor:
Rafael Marini, Mihir Sahasrabudhe, Anh Tuan Dinh-Xuan, Guillaume Chassagnon, Marie-Pierre Revel, Trieu-Nghi Hoang-Thi, Luc Mouthon, Alexis Régent, Bertrand Dunogué, Maria Vakalopoulou, Nikos Paragios
Publikováno v:
Radiology
Radiology, Radiological Society of North America, 2021, 298 (1), pp.189-198. ⟨10.1148/radiol.2020200319⟩
Radiology, 2021, 298 (1), pp.189-198. ⟨10.1148/radiol.2020200319⟩
Radiology, Radiological Society of North America, 2021, 298 (1), pp.189-198. ⟨10.1148/radiol.2020200319⟩
Radiology, 2021, 298 (1), pp.189-198. ⟨10.1148/radiol.2020200319⟩
International audience; In patients with systemic sclerosis, a deep learning classifier applied to elastic registration of chest CT images depicted lung shrinkage and functional worsening with high accuracy.BackgroundLongitudinal follow-up of interst
Autor:
Théophraste Henry, Enzo Battistella, Théo Estienne, Alexandre Carré, Eric Deutsch, Marion Classe, Julien Adam, Maria Vakalopoulou, Nikos Paragios, Marvin Lerousseau
Publikováno v:
MICCAI 2020-Medical Image Computing and Computer Assisted Intervention
MICCAI 2020-Medical Image Computing and Computer Assisted Intervention, Oct 2020, Lima, Peru. pp.470-479, ⟨10.1007/978-3-030-59722-1_45⟩
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597214
MICCAI (5)
MICCAI 2020-Medical Image Computing and Computer Assisted Intervention, Oct 2020, Lima, Peru. pp.470-479, ⟨10.1007/978-3-030-59722-1_45⟩
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597214
MICCAI (5)
Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on hand-crafted annotations which hinder clinical translation since hist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e515eb75045eae554dc7979019d912ef
https://hal.archives-ouvertes.fr/hal-03133239
https://hal.archives-ouvertes.fr/hal-03133239
Autor:
Roberto Salgado, Stefan Michiels, Stergios Christodoulidis, Fabrice Andre, Maria Vakalopoulou, Mihir Sahasrabudhe, Sherene Loi, Nikos Paragios
Publikováno v:
MICCAI 2020-23rd International Conference on Medical Image Computing and Computer Assisted Intervention
MICCAI 2020-23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2020, Lima, Peru. pp.393-402, 2020, ⟨10.1007/978-3-030-59722-1_38⟩
MICCAI 2020-International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI 2020-International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct 2020, Lima, Peru. pp.393-402, ⟨10.1007/978-3-030-59722-1_38⟩
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597214
MICCAI (5)
MICCAI 2020-23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2020, Lima, Peru. pp.393-402, 2020, ⟨10.1007/978-3-030-59722-1_38⟩
MICCAI 2020-International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI 2020-International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct 2020, Lima, Peru. pp.393-402, ⟨10.1007/978-3-030-59722-1_38⟩
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597214
MICCAI (5)
Segmentation and accurate localization of nuclei in histopathological images is a very challenging problem, with most existing approaches adopting a supervised strategy. These methods usually rely on manual annotations that require a lot of time and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b33c8799d4b67fddc5f64ff36d2be60
https://hal.archives-ouvertes.fr/hal-03087006
https://hal.archives-ouvertes.fr/hal-03087006
Autor:
Angela Rouyar, Sophie Bockel, Christophe Massard, Benjamin Frey, Stéphane Niyoteka, Charles Ferté, Enzo Battistella, Edouard Romano, Eric Deutsch, Rainer Fietkau, Guillaume Louvel, Emilie Alvarez Andres, Charlotte Robert, Markus Hecht, Udo S. Gaipl, A. Carré, Marina Milic, Jerome Durand-Labrunie, Celine Boutros, Roger Sun, Andrea Lancia, Jean-Charles Soria, Rastilav Bahleda, Nora Sundahl, Florian Putz, Piet Ost, Maria Vakalopoulou, Nikos Paragios
Publikováno v:
Journal for Immunotherapy of Cancer
Journal for Immunotherapy of Cancer, 2020, 8 (2), pp.e001429. ⟨10.1136/jitc-2020-001429⟩
Journal for ImmunoTherapy of Cancer, Vol 8, Iss 2 (2020)
Journal for Immunotherapy of Cancer, BMJ Publishing Group 2020, 8 (2), pp.e001429. ⟨10.1136/jitc-2020-001429⟩
Journal for Immunotherapy of Cancer, 2020, 8 (2), pp.e001429. ⟨10.1136/jitc-2020-001429⟩
Journal for ImmunoTherapy of Cancer, Vol 8, Iss 2 (2020)
Journal for Immunotherapy of Cancer, BMJ Publishing Group 2020, 8 (2), pp.e001429. ⟨10.1136/jitc-2020-001429⟩
BackgroundCombining radiotherapy (RT) with immuno-oncology (IO) therapy (IORT) may enhance IO-induced antitumor response. Quantitative imaging biomarkers can be used to provide prognosis, predict tumor response in a non-invasive fashion and improve p