Výsledky vyhledávání - "Marvin Lerousseau"

Akademický článek

Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation

Autor: Théo Estienne, Marvin Lerousseau, Maria Vakalopoulou, Emilie Alvarez Andres, Enzo Battistella, Alexandre Carré, Siddhartha Chandra, Stergios Christodoulidis, Mihir Sahasrabudhe, Roger Sun, Charlotte Robert, Hugues Talbot, Nikos Paragios, Eric Deutsch

Publikováno v: Frontiers in Computational Neuroscience, Vol 14 (2020)

Image registration and segmentation are the two most studied problems in medical image analysis. Deep learning algorithms have recently gained a lot of attention due to their success and state-of-the-art results in variety of problems and communities

Externí odkaz: https://doaj.org/article/5c3915bdf829480aa15d8fa0eb924aa6

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Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy

Autor: Roger Sun, Marvin Lerousseau, Jade Briend-Diop, Emilie Routier, Severine Roy, Théophraste Henry, Kanta Ka, Rui Jiang, Nawal Temar, Alexandre Carré, Adrien Laville, Anthony Hamaoui, Pierre-Antoine Laurent, Angela Rouyar, Charlotte Robert, Caroline Robert, Eric Deutsch

Publikováno v: Journal for immunotherapy of cancer. 10(10)

PurposeWhile there is still a significant need to identify potential biomarkers that can predict which patients are most likely to respond to immunotherapy treatments, radiomic approaches have shown promising results. The objectives of this study wer

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f0586d00d4ada8cd535ed6ad64bf21d
https://pubmed.ncbi.nlm.nih.gov/36307149

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Radiomics analysis and metastatic patients: can we really continue to sidestep intra-patient tumor heterogeneity ?

Autor: Théophraste Henry, Roger Sun, Marvin Lerousseau, Théo Estienne, Charlotte Robert, Benjamin Besse, Caroline Robert, Nikos Paragios, Eric Deutsch

After promising results for localized cancer disease, radiomics analysis has been applied to the metastatic setting, sidestepping the potential intertumoral intrapatient heterogeneity captured by radiomics features. Our aim was to evaluate if radiomi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::07096c2ce235f2916f2dffa652d00e8a
https://doi.org/10.21203/rs.3.rs-1775619/v1

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Perspectives in pathomics in head and neck cancer

Autor: Eric Deutsch, Jean-Yves Scoazec, Marvin Lerousseau, Marion Classe

Publikováno v: Current Opinion in Oncology. 33:175-183

Purpose of review Pathology is the cornerstone of cancer care. Pathomics, which represents the use of artificial intelligence in digital pathology, is an emerging and promising field that will revolutionize medical and surgical pathology in the comin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f07a13f60a538e6435961f3d57d424ed
https://doi.org/10.1097/cco.0000000000000731

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PH-0652 Synthetic CT from MRI with deep learning: Assessing the clinical impact of generated errors

Autor: B. Biron, S. Corbin, Théo Estienne, Théophraste Henry, F. Auville, C. Veres, Frédéric Dhermain, Eric Deutsch, C. Robert, A. Gasnier, N. Paragios, E. Alvarez Andres, A. Vatonne, Lucas Fidon, Marvin Lerousseau

Publikováno v: Radiotherapy and Oncology. 161:S520-S522

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fc8ab5f265d937916c47a1221c8adc10
https://doi.org/10.1016/s0167-8140(21)07384-9

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End-to-End Multi-Slice-to-Volume Concurrent Registration and Multimodal Generation

Autor: Amaury Leroy, Marvin Lerousseau, Théophraste Henry, Alexandre Cafaro, Nikos Paragios, Vincent Grégoire, Eric Deutsch

Publikováno v: Lecture Notes in Computer Science ISBN: 9783031164453

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::266bf423b7bc5a232cec48f3c91677f7
https://doi.org/10.1007/978-3-031-16446-0_15

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Weakly supervised pan-cancer segmentation tool

Autor: Marvin Lerousseau, Théophraste Henry, Enzo Battistella, Amaury Leroy, Marion Classe, Maria Vakalopoulou, Théo Estienne, Jean-Yves Scoazec, Nikos Paragios, Roger Sun, Eric Deutsch

Publikováno v: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Medical Image Computing and Computer Assisted Intervention
Medical Image Computing and Computer Assisted Intervention, Sep 2021, Stansbroug, France. pp.248-256, ⟨10.1007/978-3-030-87237-3_24⟩
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872366
MICCAI (8)

The vast majority of semantic segmentation approaches rely on pixel-level annotations that are tedious and time consuming to obtain and suffer from significant inter and intra-expert variability. To address these issues, recent approaches have levera

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ffb99da2af825c7ca41875aae598660
http://arxiv.org/abs/2105.04269

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Exploring Deep Registration Latent Spaces

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

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

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Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution

Autor: Théo Estienne, Charlotte Robert, Alexandre Carré, Eric Deutsch, Nikos Paragios, Théophraste Henry, Marvin Lerousseau

Publikováno v: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783030720834
BrainLes@MICCAI (1)

Brain tumor segmentation is a critical task for patient’s disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly with deep supervision and stochastic weight averaging, on the Multi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2cd8291af846fac553437b31b0d65206
https://doi.org/10.1007/978-3-030-72084-1_30

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