Search Results - "Lerousseau, Marvin"

Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics by Carré, Alexandre, Klausner, Guillaume, Edjlali, Myriam, Lerousseau, Marvin, Briend-Diop, Jade, Sun, Roger, Ammari, Samy, Reuzé, Sylvain, Alvarez Andres, Emilie, Estienne, Théo, Niyoteka, Stéphane, Battistella, Enzo, Vakalopoulou, Maria, Dhermain, Frédéric, Paragios, Nikos, Deutsch, Eric, Oppenheim, Catherine, Pallud, Johan, Robert, Charlotte

“…Radiomics relies on the extraction of a wide variety of quantitative image-based features to provide decision support. Magnetic resonance imaging (MRI)…”
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Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation by Estienne, Théo, Lerousseau, Marvin, Vakalopoulou, Maria, Alvarez Andres, Emilie, Battistella, Enzo, Carré, Alexandre, Chandra, Siddhartha, Christodoulidis, Stergios, Sahasrabudhe, Mihir, Sun, Roger, Robert, Charlotte, Talbot, Hugues, Paragios, Nikos, Deutsch, Eric

“…Image registration and segmentation are the two most studied problems in medical image analysis. Deep learning algorithms have recently gained a lot of…”
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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 by Sun, Roger, Lerousseau, Marvin, Briend-Diop, Jade, Routier, Emilie, Roy, Severine, Henry, Théophraste, Ka, Kanta, Jiang, Rui, Temar, Nawal, Carré, Alexandre, Laville, Adrien, Hamaoui, Anthony, Laurent, Pierre-Antoine, Rouyar, Angela, Robert, Charlotte, Robert, Caroline, Deutsch, Eric

“…PurposeWhile there is still a significant need to identify potential biomarkers that can predict which patients are most likely to respond to immunotherapy…”
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Learning to run a power network challenge for training topology controllers by Marot, Antoine, Donnot, Benjamin, Romero, Camilo, Donon, Balthazar, Lerousseau, Marvin, Veyrin-Forrer, Luca, Guyon, Isabelle

“…•it presents the L2RPN challenge, a competition in which players have to design strategies to optimize the topology of a power systems network,•it details the…”
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Perspectives in pathomics in head and neck cancer by Classe, Marion, Lerousseau, Marvin, Scoazec, Jean-Yves, Deutsch, Eric

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Perspectives in pathomics in head and neck cancer by Classe, Marion, Lerousseau, Marvin, Scoazec, Jean-Yves, Deutsch, Eric

“…Pathology is the cornerstone of cancer care. Pathomics, which represents the use of artificial intelligence in digital pathology, is an emerging and promising…”
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Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning by Alvarez Andres, Emilie, Fidon, Lucas, Vakalopoulou, Maria, Lerousseau, Marvin, Carré, Alexandre, Sun, Roger, Klausner, Guillaume, Ammari, Samy, Benzazon, Nathan, Reuzé, Sylvain, Estienne, Théo, Niyoteka, Stéphane, Battistella, Enzo, Rouyar, Angéla, Noël, Georges, Beaudre, Anne, Dhermain, Frédéric, Deutsch, Eric, Paragios, Nikos, Robert, Charlotte

“…This study aims to evaluate the impact of key parameters on the pseudo computed tomography (pCT) quality generated from magnetic resonance imaging (MRI) with a…”
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Models including pathological and radiomic features vs clinical models in predicting outcome of patients with metastatic non-small cell lung cancer treated with immunotherapy by Captier, Nicolas, Lerousseau, Marvin, Orlhac, Fanny, Hovhannisyan, Narinée, Luporsi, Marie, Lagha, Sarah, Tedesco, Anne-Sophie, Salamoun Feghali, Paulette, Lonjou, Christine, Ramtohul, Toulsie, Beaulaton, Clément, Brisse, Hervé, Vincent-Salomon, Anne, Walter, Thomas, Buvat, Irène, Girard, Nicolas, Barillot, Emmanuel

“…e21164 Background: Overall survival of patients with metastatic non-small cell lung cancer (NSCLC) has increased with the use of anti-PD-1 immune checkpoint…”
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Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group by Thagaard, Jeppe, Broeckx, Glenn, Page, David B, Verbandt, Sara, Kos, Zuzana, Gupta, Rajarsi, Khiroya, Reena, Abduljabbar, Khalid, Acosta Haab, Gabriela, Acs, Balazs, Almeida, Jonas S, Alvarado‐Cabrero, Isabel, Azmoudeh‐Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Bellolio, Enrique R, Bheemaraju, Vydehi, Blenman, Kim RM, Bouchmaa, Najat, Cooper, Lee AD, Coosemans, An, Dahl, Anders B, Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Doré Hansen, Johan, Dudgeon, Sarah N, Elghazawy, Mahmoud, Fernandez‐Martín, Claudio, Fox, Stephen B, Giltnane, Jennifer M, Gonzalez‐Ericsson, Paula I, Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G, Harbhajanka, Aparna, Hartman, Johan, Hauberg, Søren, Hewitt, Stephen, Hida, Akira I, Horlings, Hugo M, Hytopoulos, Evangelos, Janssen, Emiel AM, Kahila, Mohamed, Kataoka, Tatsuki R, Kawaguchi, Kosuke, Kharidehal, Durga, Kiraz, Umay, Kodach, Liudmila L, Korski, Konstanty, Kovács, Anikó, Laenkholm, Anne‐Vibeke, Lang‐Schwarz, Corinna, Lennerz, Jochen K, Li, Xiaoxian, Maley, Sai K, Manur Narasimhamurthy, Vidya, Mehrotra, Ravi, Michiels, Stefan, Minhas, Fayyaz ul Amir Afsar, Moore, David A, Mushtaq, Shamim, Papathomas, Thomas, Penault‐Llorca, Frederique, Pinard, Christopher J, Pruneri, Giancarlo, Rahman, Arman, Rau, Tilman T, Reis‐Filho, Jorge S, Rimm, David, Roslind, Anne, Vincent‐Salomon, Anne, Salto‐Tellez, Manuel, Saltz, Joel, Sayed, Shahin, Scott, Ely, Sur, Daniel, Symmans, Fraser, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E Aubrey, Tramm, Trine, Tran, William T, Verghese, Gregory E, Vieth, Michael, Wahab, Noorul, Walter, Thomas, Waumans, Yannick, Wen, Hannah Y, Yuan, Yinyin, Zin, Reena Md, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Salgado, Roberto, Specht Stovgaard, Elisabeth

“…The clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as…”
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Image‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer by Jahangir, Chowdhury Arif, Page, David B, Broeckx, Glenn, Gonzalez, Claudia A, Burke, Caoimbhe, Reis‐Filho, Jorge S, Ly, Amy, Harms, Paul W, Vieth, Michael, Kos, Zuzana, Diest, Paul J, Thagaard, Jeppe, Khiroya, Reena, Acosta Haab, Gabriela, Acs, Balazs, Adams, Sylvia, Almeida, Jonas S, Alvarado‐Cabrero, Isabel, Azmoudeh‐Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Bellolio, Enrique R, Blenman, Kim RM, Cheang, Maggie Chon U, Ciompi, Francesco, Cooper, Lee AD, Coosemans, An, Corredor, Germán, Dantas Portela, Flavio Luis, Demaria, Sandra, Dudgeon, Sarah N, Elghazawy, Mahmoud, Fernandez‐Martín, Claudio, Fineberg, Susan, Fox, Stephen B, Giltnane, Jennifer M, Gnjatic, Sacha, Grigoriadis, Anita, Hanna, Matthew G, Harbhajanka, Aparna, Hart, Steven N, Hartman, Johan, Hewitt, Stephen, Irshad, Sheeba, Janssen, Emiel AM, Kataoka, Tatsuki R, Kawaguchi, Kosuke, Khramtsov, Andrey I, Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L, Korski, Konstanty, Akturk, Guray, Scott, Ely, Lænkholm, Anne‐Vibeke, Lennerz, Jochen K, Lerousseau, Marvin, Li, Xiaoxian, Maley, Sai K, Manur Narasimhamurthy, Vidya, Marks, Douglas K, Mehrotra, Ravi, Michiels, Stefan, Minhas, Fayyaz ul Amir Afsar, Moore, David A, Mushtaq, Shamim, Nighat, Hussain, Papathomas, Thomas, Penault‐Llorca, Frederique, Pinard, Christopher J, Pinto‐Cardenas, Juan Carlos, Pruneri, Giancarlo, Rau, Tilman T, Rimm, David, Vincent‐Salomon, Anne, Saltz, Joel, Sayed, Shahin, Hytopoulos, Evangelos, Sotiriou, Christos, Sur, Daniel, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E Aubrey, Tramm, Trine, Tran, William T, Laak, Jeroen, Verghese, Gregory E, Viale, Giuseppe, Wahab, Noorul, Walter, Thomas, Wen, Hannah Y, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Specht Stovgaard, Elisabeth, Rahman, Arman

“…Recent advances in the field of immuno‐oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has…”
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COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures by Battistella, Enzo, Vakalopoulou, Maria, Sun, Roger, Estienne, Theo, Lerousseau, Marvin, Nikolaev, Sergey, Andres, Emilie Alvarez, Carre, Alexandre, Niyoteka, Stephane, Robert, Charlotte, Paragios, Nikos, Deutsch, Eric

“…Precision medicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of…”
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Design and implementation of an environment for Learning to Run a Power Network (L2RPN) by Lerousseau, Marvin

“…This report summarizes work performed as part of an internship at INRIA, in partial requirement for the completion of a master degree in math and informatics…”
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Giga-SSL: Self-Supervised Learning for Gigapixel Images by Lazard, Tristan, Lerousseau, Marvin, Decenciere, Etienne, Walter, Thomas

“…Whole slide images (WSI) are microscopy images of stained tissue slides routinely prepared for diagnosis and treatment selection in medical practice. WSI are…”
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Giga-SSL: Self-Supervised Learning for Gigapixel Images by Lazard, Tristan, Lerousseau, Marvin, Decencière, Etienne, Walter, Thomas

“…Whole slide images (WSI) are microscopy images of stained tissue slides routinely prepared for diagnosis and treatment selection in medical practice. WSI are…”
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Multimodal brain tumor classification by Lerousseau, Marvin, Deutsh, Eric, Paragios, Nikos

“…Cancer is a complex disease that provides various types of information depending on the scale of observation. While most tumor diagnostics are performed by…”
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SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification by Lerousseau, Marvin, Vakalopoulou, Maria, Deutsch, Eric, Paragios, Nikos

“…Multiple instance learning (MIL) is the preferred approach for whole slide image classification. However, most MIL approaches do not exploit the…”
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MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network by Estienne, Théo, Vakalopoulou, Maria, Battistella, Enzo, Henry, Theophraste, Lerousseau, Marvin, Leroy, Amaury, Paragios, Nikos, Deutsch, Eric

“…Deformable registration consists of finding the best dense correspondence between two different images. Many algorithms have been published, but the clinical…”
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Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution by Henry, Theophraste, Carre, Alexandre, Lerousseau, Marvin, Estienne, Theo, Robert, Charlotte, Paragios, Nikos, Deutsch, Eric

“…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…”
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Deep learning based registration using spatial gradients and noisy segmentation labels by Estienne, Théo, Vakalopoulou, Maria, Battistella, Enzo, Carré, Alexandre, Henry, Théophraste, Lerousseau, Marvin, Robert, Charlotte, Paragios, Nikos, Deutsch, Eric

“…In: Shusharina N., Heinrich M.P., Huang R. (eds) Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. MICCAI 2020. Lecture…”
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Learning to run a power network challenge for training topology controllers by Marot, Antoine, Donnot, Benjamin, Romero, Camilo, Veyrin-Forrer, Luca, Lerousseau, Marvin, Donon, Balthazar, Guyon, Isabelle

“…For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities. However,…”
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