Select a category

2022

Evaluation of AI vs. Clinical Experts SBRT-Thorax Computed Tomography OARs Delineation

Stathakis, S. & Pissakas, Georgios & Alexiou, A. & Bertrand, B. & Bondiau, P.Y. & Claude, Lekunze & Cuthbert, T. & Damatopoulou, A. & Dejean, C. & Doukakis, C. & Gungor, Gorkem & Hardy, L. & Maani, E. & Martel-Lafay, Isabelle & Mavroidis, P. & Paragios, N. & Peppa, Vasiliki & Remonde, D. & Shumway, J.W. & Ozyar, Enis. (2022). Evaluation of AI vs. Clinical Experts SBRT-Thorax Computed Tomography OARs Delineation. International Journal of Radiation Oncology*Biology*Physics. 114. e102-e103. 10.1016/j.ijrobp.2022.07.897.

AI-based OAR annotation for pediatric brain radiotherapy planning

Bondiau, P. & Bolle, S. & Escande, Alexandre & Duverge, L. & Demoor, C. & Rouyar-Nicolas, A. & Bertrand, B. & Cannard, A. & Hardy, L. & Martineau-Huynh, C. & Paragios, N. & Roque, T. & Deutsch, E. & Robert, Charlotte. (2022). PD-0330 AI-based OAR annotation for pediatric brain radiotherapy planning. Radiotherapy and Oncology. 170. S293-S295. 10.1016/S0167-8140(22)02823-7.

Statistical discrepancies in GTV delineation for H&N cancer across expert centers

Leroy, A. & Paragios, Nikos & Deutsch, E. & Grégoire, V. & Mitrea, D. & Pêtre, A. & Sun, Roger & Tao, Y.G.. (2022). MO-0476 Statistical discrepancies in GTV delineation for H&N cancer across expert centers. Radiotherapy and Oncology. 170. S426-S427. 10.1016/S0167-8140(22)02370-2.

AI-based cardiac annotation for radiotherapy planning

Botticella, A. & Loap, P. & De Marzi, L. & Lévy, A. & Martin, V. & Moukasse, Y. & Bolle, S. & Rouyar-Nicolas, A. & Le péchoux, C. & Luo, C. & Colame, S. & Martineau-Huynh, C. & Oumani, A. & Roque, T. & Deutsch, E. & Robert, C. & Rivera, S. & Kirova, Y. (2022). AI-based cardiac annotation for radiotherapy planning. SFRO

2021

To plan and deliver adjuvant breast radiotherapy over 1 week: 1-week breast workflow implementation

Louvel, G. & Milewski, C. & Auzac, Guillaume & Villaret, F. & Ung, M. & Berthelot, K. & Folino, E. & Ezra, P. & Roberti, E. & Yessoufou, I. & Cheve, M. & Fournier-Bidoz, N. & Paragios, Nikos & Deutsch, E. & Rivera, S.. (2021). PO-1099 To plan and deliver adjuvant breast radiotherapy over 1 week: 1-week breast workflow implementation. Radiotherapy and Oncology. 161. S914-S915. 10.1016/S0167-8140(21)07550-2.

Improvement of a deep learning based automatic delineation model using anatomical criteria

Brion, T. & Karamouza, E. & Vitry, L. & Lombard, A. & Roque, T. & Paragios, Nikos & Auzac, Guillaume & Lamrani-Ghaouti, A. & Bonnet, N. & Limkin, Elaine & Ung, M. & Bockel, S. & Pasquier, D. & Wong, S. & trialists, H. & Achkar, S. & Rivera, S.. (2021). PD-0731 Improvement of a deep learning based automatic delineation model using anatomical criteria. Radiotherapy and Oncology. 161. S561-S563. 10.1016/S0167-8140(21)07010-9.

Quality Assurance and Clinical Acceptability for AI-driven Automatic Contouring of Organs at Risk

Dissler, N. & Stathakis, S. & Lombard, A. & Paragios, Nikos & Klausner, Guillaume & Lahmi, Lucien & III, W. & Maani, E.. (2021). OC-0504 Quality Assurance and Clinical Acceptability for AI-driven Automatic Contouring of Organs at Risk. Radiotherapy and Oncology. 161. S386-S387. 10.1016/S0167-8140(21)06930-9.

Weakly Supervised Pan-Cancer Segmentation Tool

Lerousseau, Marvin & Classe, Marion & Battistella, Enzo & Estienne, Théo & Henry, Théophraste & Leroy, Amaury & Sun, Roger & Vakalopoulou, Maria & Scoazec, Jean-Yves & Deutsch, Eric & Paragios, Nikos. (2021). Weakly Supervised Pan-Cancer Segmentation Tool. 10.1007/978-3-030-87237-3_24.

2020

Deep learning auto contouring of OAR for HN radiotherapy: a blinded evaluation by clinical experts

Grégoire, V. & Blanchard, Pierre & Allajbej, A. & Petit, Claire & Milhade, N. & Nguyen, F. & Bakkar, S. & Boulle, G. & Romano, E. & Zrafi, W.s & Lombard, A. & Ullmann, E. & Paragios, Nikos & Deutsch, E. & Robert, Charlotte. (2020). OC-0681: Deep learning auto contouring of OAR for HN radiotherapy: a blinded evaluation by clinical experts. Radiotherapy and Oncology. 152. S379-S380. 10.1016/S0167-8140(21)00703-9.

Improving Radiotherapy Workflow Through Implementation of Delineation Guidelines & AI-Based Annotation

Ung, M. & Rouyar-Nicolas, A. & Limkin, Elaine & Petit, Claire & Sarrade, T. & Carre, Alexandre & Auzac, Guillaume & Lombard, A. & Ullman, E. & Bonnet, N. & Assia, L.G. & Paragios, Nikos & Huynh, C. & Deutsch, E. & Rivera, S. & Robert, Charlotte. (2020). Improving Radiotherapy Workflow Through Implementation of Delineation Guidelines & AI-Based Annotation. International Journal of Radiation Oncology*Biology*Physics. 108. e315. 10.1016/j.ijrobp.2020.07.753.

Dosimetric impact of an AI-based delineation software satisfying international guidelines in breast cancer radiotherapy

Ung, M. & Rivera, S. & Rouyar, A. & Limkin, Elaine & Petit, C. & Sarrade, T. & Carre, Alexandre & Auzac, Guillaume & Lombard, A. & Ullmann, E. & Bonnet, N. & Lamrani-Ghaouti, A. & Paragios, Nikos & Martineau-Huynh, C. & Deutsch, E. & Robert, Charlotte. (2020). Dosimetric impact of an AI-based delineation software satisfying international guidelines in breast cancer radiotherapy. European Journal of Cancer. 138. S113. 10.1016/S0959-8049(20)30840-6.

Full-body delineation of ROIs through anatomy-preserving deep learning ensemble networks

Lombard, A. & D’Avaucourt, L. & Ullmann, E. & Marini-Silva, R. & Bus, N. & Paragios, Nikos. (2020). PO-1753: Full-body delineation of ROIs through anatomy-preserving deep learning ensemble networks. Radiotherapy and Oncology. 152. S974-S975. 10.1016/S0167-8140(21)01771-0.

AI-driven quality insurance for delineation in radiotherapy breast clinical trials

Rivera, S. & Lombard, A. & Pasquier, D. & Wong, S. & Limkin, Elaine & Auzac, Guillaume & Blanchecotte, J. & Chand-Fouché, M.E. & Lamrani-Ghaouti, A. & Bonnet, N. & Paragios, Nikos & Martineau-Huynh, C. & Ullmann, E. & Ruffier, A. & Deutsch, E.. (2020). PO-1722: AI-driven quality insurance for delineation in radiotherapy breast clinical trials. Radiotherapy and Oncology. 152. S953. 10.1016/S0167-8140(21)01740-0.

A blinded prospective evaluation of clinical applicability of deep learning-based auto contouring of OAR for Head & Neck radiotherapy

Blanchard, Pierre & Gregoire, V.G. & Petit, Claire & Milhade, N. & Allajbej, A. & Nguyen, T.V.F. & Bakkar, S. & Boulle, G. & Lombard, A. & Beldjoudi, G. & Munoz, A. & Ullman, E. & Paragios, Nikos & Deutsch, E. & Robert, Charlotte. (2020). A Blinded Prospective Evaluation Of Clinical Applicability Of Deep Learning-Based Auto Contouring Of OAR For Head and Neck Radiotherapy. International Journal of Radiation Oncology*Biology*Physics. 108. e780-e781. 10.1016/j.ijrobp.2020.07.239.

2023

Bridging the gap between radiology and histology through AI-driven registration and reconstruction

Leroy, Amaury & Cafaro, Alexandre & Lepetit, Vincent & Paragios, Nikos & Deutsch, Eric & Grégoire, Vincent. 2023. Bridging the gap between radiology and histology through AI-driven registration and reconstruction. ESTRO

2022

AI-driven combined deformable registration and image synthesis between radiology and histopathology

Leroy, A. & Lerousseau, Marvin & Henry, Théophraste & Estienne, Théo & Classe, M. & Paragios, N. & Deutsch, E. & Grégoire, V.. (2022). PO-1613 AI-driven combined deformable registration and image synthesis between radiology and histopathology. Radiotherapy and Oncology. 170. S1400-S1401. 10.1016/S0167-8140(22)03577-0.

2021

Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels

Estienne, Théo & Vakalopoulou, Maria & Battistella, Enzo & Carre, Alexandre & Henry, Théophraste & Lerousseau, Marvin & Robert, Charlotte & Paragios, Nikos & Deutsch, Eric. (2021). Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels. 10.1007/978-3-030-71827-5_11.

Elastic Registration–driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT

Chassagnon, Guillaume & Vakalopoulou, Maria & Regent, Alexis & Sahasrabudhe, Mihir & Marini, Rafael & TN, Hoang & Dinh-Xuan, Anh Tuan & Dunogué, Bertrand & Mouthon, Luc & Paragios, Nikos & Revel, Marie-Pierre. (2020). Elastic Registration–driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT. Radiology. 298. 200319. 10.1148/radiol.2020200319.

Exploring Deep Registration Latent Spaces

Estienne, Théo & Vakalopoulou, Maria & Christodoulidis, Stergios & Battistella, Enzo & Henry, Théophraste & Lerousseau, Marvin & Leroy, Amaury & Chassagnon, Guillaume & Revel, Marie-Pierre & Paragios, Nikos & Deutsch, Eric. (2021). Exploring Deep Registration Latent Spaces. 10.1007/978-3-030-87722-4_11.

2019

Image Registration of Satellite Imagery with Deep Convolutional Neural Networks

Vakalopoulou, Maria & Christodoulidis, Stergios & Sahasrabudhe, Mihir & Mougiakakou, Stavroula & Paragios, Nikos. (2019). Image Registration of Satellite Imagery with Deep Convolutional Neural Networks. 4939-4942. 10.1109/IGARSS.2019.8898220.

2018

Weakly Supervised Learning of Metric Aggregations for Deformable Image Registration

Ferrante, Enzo & Dokania, Puneet & Silva, Rafael & Paragios, Nikos. (2018). Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration. IEEE journal of biomedical and health informatics. PP. 10.1109/JBHI.2018.2869700.

2023

Evaluation of AI based synthetic CT generation for T2w pelvis MRIs

Shreshtha, Kumar & Alongi, Filippo & Rigo, Michele & Pellegrini, Roberto Giuseppe & Roque, Thais & Paragios, Nikos & Ruggieri, Ruggero. 2023 Evaluation of AI based synthetic CT generation for T2w pelvis MRIs. ESTRO

Evaluation of AI based CT auto-contouring of synthetic CTs as an indicator for synthesis quality

Shreshtha, Kumar & Rigo, Michele & Alongi, Filippo & Pellegrini, Roberto Giuseppe & Roque, Thais & Paragios, Nikos & Ruggieri, Ruggero. 2023 Evaluation of AI based CT auto-contouring on synthetic CTs as an indicator for synthesis quality. ESTRO

Evaluation of a new AI-based sCT generator for MR-only radiotherapy workflows

Roussel, Alban & Dufreneix, Stephane & Cannard, Arthus & Roque, Thais & Shreshtha, Kumar & Paragios, Nikos & Guillerminet, Camille & Autret, Damien. 2023. Evaluation of a new AI-based sCT generator for MR-only radiotherapy workflow. ESTRO

2022

Dosimetric evaluation of AI-based synthetic CTs for MRI-only brain radiotherapy

Veres, Cristina & Shrestha, K. & Roque, T. & Alvarez-Andres, E. & Gasnier, A. & Dhermain, Frédéric & Paragios, N. & Deutsch, E. & Robert, Charlotte. (2022). PO-1661 Dosimetric evaluation of AI-based synthetic CTs for MRI-only brain radiotherapy. Radiotherapy and Oncology. 170. S1459-S1460. 10.1016/S0167-8140(22)03625-8.

Dosimetric Evaluation of Dose Calculation Uncertainties for MR-Only Approaches in Prostate MR-Guided Radiotherapy

Coric, Ivan & Shreshtha, Kumar & Roque, Thais & Paragios, Nikos & Gani, Cihan & Zips, Daniel & Thorwarth, Daniela & Nachbar, Marcel. (2022). Dosimetric Evaluation of Dose Calculation Uncertainties for MR-Only Approaches in Prostate MR-Guided Radiotherapy. Frontiers in Physics. 10. 897710. 10.3389/fphy.2022.897710

Characterisation of synthetic CTs clinical quality: which gamma indices to evaluate in practice?

Andres, E. & Gasnier, A. & Veres, Cristina & Dhermain, Frédéric & Corbin, S. & Auville, F. & Biron, B. & Vatonne, A. & Henry, Théophraste & Estienne, Théo & Lerousseau, Marvin & Carre, Alexandre & Fidon, Lucas & Deutsch, E. & Paragios, N. & Robert, Charlotte. (2022). PO-1623 Characterisation of synthetic CTs clinical quality: which gamma indices to evaluate in practice?. Radiotherapy and Oncology. 170. S1413-S1415. 10.1016/S0167-8140(22)03587-3.

Dosimetric evaluation of dose calculation uncertainties for MR-only treatments of pelvic MRgRT

Coric, I. & Shrestha, K. & Roque, T. & Paragios, N. & Zips, D. & Thorwarth, Daniela & Nachbar, Marcel. (2022). OC-0289 Dosimetric evaluation of dose calculation uncertainties for MR-only treatments of pelvic MRgRT. Radiotherapy and Oncology. 170. S250-S251. 10.1016/S0167-8140(22)02547-6.

Clinical evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic MR

Fenoglietto, Pascal & Gevaert, Thierry & Boussaer, M. & Delasalles, E. & Ioannidou, D. & Shreshtha, K. & Roque, T. & Paragios, N. & Azria, D. & Ozyar, Enis & Gungor, Gorkem. (2022). MO-0648 Clinical evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic MR. Radiotherapy and Oncology. 170. S583-S584. 10.1016/S0167-8140(22)02406-9.

AI-based OAR delineation in brain T1w-MRI: Overcoming Inter- and Intra-observer variability

Gungor, Gorkem & Klausner, Guillaume & Gur, Giyora & Serbez, I. & Temur, B. & Cafaro, Alexandre & Hardy, L. & Kandiban, S. & Oumani, A. & Bertrand, B. & Shreshtha, K. & Roque, T. & Atalar, Banu & Paragios, N. & Ozyar, Enis. (2022). PO-1890 AI-based OAR delineation in brain T1w-MRI: Overcoming Inter- and Intra-observer variability. Radiotherapy and Oncology. 170. S1674-S1675. 10.1016/S0167-8140(22)03853-1.

Clinical evaluation of organs at risk automatic-segmentation for T2-weigthed MRI

Newman, N. & Stathakis, S. & Thorwarth, Daniela & Zips, D. & Nachbar, Marcel & Kandiban, S. & Oumani, A. & Shreshtha, K. & Roque, T. & Paragios, N. & Jones, W.E.. (2022). PD-0332 Clinical evaluation of organs at risk automatic-segmentation for T2-weigthed MRI. Radiotherapy and Oncology. 170. S296-S297. 10.1016/S0167-8140(22)02825-0.

AI surpassing human expert: a multi-centric evaluation for organ at risk delineation

Azria, D. & Boldrini, Luca & de ridder, Mark & Fenoglietto, Pascal & Gambacorta, Maria & Gevaert, Thierry & Gungor, Gorkem & Lagerwaard, F.J. & Marciscano, Ariel & Michalet, M. & Nagar, Himanshu & Pennell, R. & Serbez, I. & Vanspeybroeck, B. & Zoto, Teuta & Cafaro, Alexandre & Hardy, L. & Kandiban, S. & Oumani, A. & Ozyar, Enis. (2022). OC-0463 AI surpassing human expert: a multi-centric evaluation for organ at risk delineation. Radiotherapy and Oncology. 170. S408-S410. 10.1016/S0167-8140(22)02599-3.

A Multi-Centric Evaluation of AI-Driven Synthetic CT Generation Form Low Field Magnetic Resonance Imaging

Gungor, Gorkem & Azria, D. & Balermpas, Panagiotis & Boldrini, Luca & Chuong, Michael & de ridder, Mark & Gevaert, Thierry & Hardy, L. & Kandiban, S. & Maingon, P. & Mittauer, K.E. & Ozyar, Enis & Paragios, N. & Pennell, R. & Placidi, L. & Shreshtha, K. & Speiser, M.P. & Tanadini-Lang, Stephanie & Valdes, S. & Fenoglietto, Pascal. (2022). A Multi-Centric Evaluation of AI-Driven Synthetic CT Generation Form Low Field Magnetic Resonance Imaging. International Journal of Radiation Oncology*Biology*Physics. 114. S163. 10.1016/j.ijrobp.2022.07.655.

A Multi-Centric Evaluation of AI-Driven OARs Low Field MRgRT Pelvic /Abdomen Contouring

Azria, D. & Andratschke, Nicolaus & Balermpas, Panagiotis & Boldrini, Luca & Bourdais, R. & Bruynzeel, Anna & Chuong, Michael & de ridder, Mark & Fenoglietto, Pascal & Gevaert, Thierry & Gungor, Gorkem & Hardy, L. & Kandiban, S. & Lagerwaard, Frank & Maingon, P. & Marciscano, Ariel & Mittauer, K.E. & Nagar, Himanshu & Paragios, N. & Ozyar, Enis. (2022). A Multi-Centric Evaluation of AI-Driven OARs Low Field MRgRT Pelvic /Abdomen Contouring. International Journal of Radiation Oncology*Biology*Physics. 114. e103. 10.1016/j.ijrobp.2022.07.898.

2021

Human-Level Precision Upper Abdominal OAR Contouring With Anatomically Preserving Deep Learning During Magnetic Resonance Imaging Guided Adaptive Radiotherapy (MRgRT)

Gungor, Gorkem & Michalet, M. & Lombard, A. & Roque, T. & Atalar, B. & Temur, B. & Serbez, I. & Azria, D. & Vitry, L. & Riou, Olivier & Paragios, N. & Ozyar, Enis & Fenoglietto, Pascal. (2021). Human-Level Precision Upper Abdominal OAR Contouring With Anatomically Preserving Deep Learning During Magnetic Resonance Imaging Guided Adaptive Radiotherapy (MRgRT). International Journal of Radiation Oncology*Biology*Physics. 111. S44-S45. 10.1016/j.ijrobp.2021.07.122.

Synthetic-CT generation from T1w brain MRIs with a cascaded GANs ensemble approach

Lombard, A. & Shreshtha, K. & Robert, Charlotte & Roque, T. & Fauchon, Francois & Noël, Ge & Paragios, Nikos & Deutsch, E.. (2021). PO-1680 Synthetic-CT generation from T1w brain MRIs with a cascaded GANs ensemble approach. Radiotherapy and Oncology. 161. S1405-S1406. 10.1016/S0167-8140(21)08131-7.

Automatic synthetic-CT generation from unpaired T2w pelvis MRIs using ensembled self-supervised GANs

Lombard, A. & Shreshtha, K. & Nachbach, M. & Roque, T. & Thorwarth, Daniela & Paragios, Nikos. (2021). PD-0754 Automatic synthetic-CT generation from unpaired T2w pelvis MRIs using ensembled self-supervised GANs. Radiotherapy and Oncology. 161. S585-S586. 10.1016/S0167-8140(21)07033-X.

Development and quantitative evaluation of AI-based pelvic MRI autocontouring for adaptive MRgRT

Nachbar, M. & Lo Russo, M. & Boeke, S. & Wegener, D. & Boldt, J. & Butzer, S. & Roque, T. & Lombard, A. & De Vitry, L. & Paragios, Nikos. & Zips, D. & Thorwarth, D. (2021). OC-0085 Development and quantitative evaluation of AI-based pelvic MRI autocontouring for adaptive MRgRT. Radiotherapy and Oncology. 161. S58-S59. 10.1016/S0167-8140(21)06779-7.

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

Andres, E. & Gasnier, A. & Veres, Cristina & Dhermain, Frédéric & Corbin, S. & Auville, F. & Biron, B. & Vatonne, A. & Henry, Théophraste & Estienne, Théo & Lerousseau, Marvin & Fidon, Lucas & Deutsch, E. & Paragios, Nikos & Robert, Charlotte. (2021). PH-0652 Synthetic CT from MRI with deep learning: Assessing the clinical impact of generated errors. Radiotherapy and Oncology. 161. S520-S522. 10.1016/S0167-8140(21)07384-9.

2020

Optimizing the generation of brain pseudo-CT from MRI based on a highly efficient 3D neural network

Andres, E. & Fidon, Lucas & Vakalopoulou, M. & Lerousseau, Marvin & Carre, Alexandre & Sun, Roger & Beaudre, A. & Deutsch, E. & Paragios, Nikos & Robert, Charlotte. (2020). PO-1702: Optimizing the generation of brain pseudo-CT from MRI based on a highly efficient 3D neural network. Radiotherapy and Oncology. 152. S938-S939. 10.1016/S0167-8140(21)01720-5.

Assessment of the generalizability to pediatric protontherapy of a 3D network generating pseudo-CT

Andres, E. & Causse, Maelie & Fidon, Lucas & Ermeneux, Louis & Bolle, S. & Martin, V. & Paragios, Nikos & Deutsch, E. & De marzi, Ludovic & Robert, Charlotte. (2020). PH-0408: Assessment of the generalizability to pediatric protontherapy of a 3D network generating pseudo-CT. Radiotherapy and Oncology. 152. S219-S220. 10.1016/S0167-8140(21)00430-8.

Training and validation of an AI-based MRI auto-contouring method for pelvic organs

Boeke, S & la Russo, M & Nachbar, M & Winter, J & Lombard, A & Bus, N & Paragios, N & Gani, C & Müller, A.C & Zips, D & Thorwarth, D (2020). VS09-5-jD: Training and validation of an AI-based MRI auto-contouring method for pelvic organs. Strahlenther Onkol. 196 (suppl 1), 1-230 (2020). 10.1007/s00066-020-01620-0.

Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning

Andres, Emilie & Fidon, Lucas & Vakalopoulou, Maria & Lerousseau, Marvin & Carre, Alexandre & Sun, Roger & Klausner, Guillaume & Ammari, S. & Benzazon, Nathan & Reuzé, Sylvain & Estienne, Théo & Niyoteka, Stéphane & Battistella, Enzo & Rouyar, Angéla & Noël, Ge & Beaudre, Anne & Dhermain, Frédéric & Deutsch, Eric & Paragios, Nikos & Robert, Charlotte. (2020). Dosimetry-driven quality measure of brain pseudo Computed Tomography generated from deep learning for MRI-only radiotherapy treatment planning. International Journal of Radiation Oncology*Biology*Physics. 108. 10.1016/j.ijrobp.2020.05.006.

2019

Pseudo Computed Tomography generation using 3D deep learning – Application to brain radiotherapy

Andres, E. & Fidon, Lucas & Vakalopoulou, M. & Noël, Ge & Niyoteka, S. & Benzazon, Nathan & Deutsch, E. & Paragios, Nikos & Robert, Charlotte. (2019). PO-1002 Pseudo Computed Tomography generation using 3D deep learning – Application to brain radiotherapy. Radiotherapy and Oncology. 133. S553. 10.1016/S0167-8140(19)31422-7.

Assessing the impact of key preprocessing concepts on the pseudo CT generation

Andres, E. & Fidon, Lucas & Vakalopoulou, M. & Noël, Ge & Beaudre, A. & Niyoteka, S. & Benzazon, Nathan & Lefkopoulos, D. & Deutsch, E. & Paragios, Nikos & Robert, Charlotte. (2019). 44 Assessing the impact of key preprocessing concepts on the pseudo CT generation. Physica Medica. 68. 27. 10.1016/j.ejmp.2019.09.125.

2023

AI-based delineation for CBCT offline adaptive radiotherapy: an interexpert variability evaluation

Roque, Thais & Agarwal, Mohit Shiv & Klausner, Guillaume & Guihard, Sebastien & Parenica, Holly M & Andersen, Samuel N & Cuthbert, Thomas Alexander & Maani, Elizabeth & Anderson, Clark & Oumani, Ayoub & Kandiban, Sanmady & Chailloleau, Pierre & Teboul, Olivier & Paragios, Nikos & Jones, William E. III & Sotirios Stathakis. 2023 AI-based delineation for CBCT offline adaptive radiotherapy: an interexpert variability evaluation. ESTRO

Can artificial intelligence bring cone beam CT acquisitions to planning CT quality?

Roque, Thais & Oumani, Ayoub & Teboul, Olivier & Paragios, Nikos & Fenoglietto, Pascal. 2023. Can artificial intelligence bring cone beam CT acquisitions to planning CT quality?. ESTRO

Clinical evaluation of fusion for offline adaptive radiotherapy: AI-based synthetic CT vs CBCT

Roque, Thais & Terlizzi, Mario & Robert, Charlotte. 2023. Clinical evaluation of fusion for offline adaptive radiotherapy: AI-based synthetic CT vs CBCT. ESTRO

Self-learning GAN based synthetic CT generation: unlocking CBCT-based adaptive radiotherapy

Roque, Thais & Oumani, Ayoub & Delasalles, Edouard & Paragios, Nikos & Fenoglietto, Pascal. 2023. Self-learning GAN based synthetic CT generation: unlocking CBCT-based adaptive radiotherapy. ESTRO

2023

Deep Particles Embedding: accelerating Monte-Carlo dose simulations

Martinot, S. & Komodalis, N. & Vakalopoulou, M. & Bus, N. & Robert, C. & Deutsch, E. & Paragios, N. (2023). Deep Particles Embedding: accelerating Monte-Carlo dose simulations. ESTRO

Automation of prostate treatment planning with template dose mimicking constraints

Vauclin, Remi & Prunaretty, Jessica & Bus, Norbert & Mengin, Elie & Marini Silva, Rafael & Kallala, Haithem & Paragios, Nikos & Fenoglietto, Pascal. 2023. Automation of prostate treatment planning with template dose mimicking constraints. ESTRO

2022

Dose Predictions for Head and Neck Cancers Using Hybrid Structure Sets Containing Manual and Automated Contours

Buatti, J.S. & Stathakis, S. & Kirby, N. & Li, R. & Oliveira, M. & Kabat, C. & Papanikolaou, N. & Paragios, N.. (2022). Dose Predictions for Head and Neck Cancers Using Hybrid Structure Sets Containing Manual and Automated Contours. International Journal of Radiation Oncology*Biology*Physics. 114. e95. 10.1016/j.ijrobp.2022.07.881.

2021

Fast Monte-Carlo dose simulation with recurrent deep learning

Martinot, S. & Bus, N. & Vakalopoulou, M. & Robert, Charline & Deutsch, E. & Paragios, Nikos. (2021). OC-0308 Fast Monte-Carlo dose simulation with recurrent deep learning. Radiotherapy and Oncology. 161. S216-S217. 10.1016/S0167-8140(21)06855-9.

DeepDoseOpt: End-to-End VMAT Pelvis Dose Prediction & Treatment Planning Inference

Dedieu, J. & Shreshtha, K. & Lombard, A. & Bus, N. & Martinot, S. & Fick, R. & Paragios, Nikos. (2021). PD-0820 DeepDoseOpt: End-to-End VMAT Pelvis Dose Prediction & Treatment Planning Inference. Radiotherapy and Oncology. 161. S652-S653. 10.1016/S0167-8140(21)07099-7.

High-particle simulation of Monte-Carlo dose distribution with 3D ConvLSTMs

Martinot, S. & Bus, N. & Vakalopoulou, M. & Robert, C. & Deutsch, E. & Paragios, N. (2021). Weakly supervised 3D ConvLSTMs for Monte-Carlo radiotherapy dose simulations. Medical Imaging with Deep Learning.

End-to-end Treatment Planning Optimization through Dose/Anatomy-based Metric-learning kNN Embeddings

Vitry, L. & Fick, R. & Bus, N. & Dedieu, J. & Lombard, A. & Paragios, Nikos. (2021). PO-1839 End-to-end Treatment Planning Optimization through Dose/Anatomy-based Metric-learning kNN Embeddings. Radiotherapy and Oncology. 161. S1568-S1569. 10.1016/S0167-8140(21)08290-6.

Weakly supervised 3D ConvLSTMs for Monte-Carlo radiotherapy dose simulations

Martinot, Sonia & Bus, Norbert & Vakalopoulou, Maria & Robert, Charlotte & Deutsch, Eric & Paragios, Nikos. (2021). High-Particle Simulation of Monte-Carlo Dose Distribution with 3D ConvLSTMs. 10.1007/978-3-030-87202-1_48.

2020

SIMSEB: Unlocking the Dosimetric Potential of Sequential Boost Plans in VMAT Through Simultaneous Optimization

Fick, R.H.J. & Boule, T. & Pouille, A. & Lombard, A. & Bus, N. & Paragios, Nikos. (2020). SIMSEB: Unlocking the Dosimetric Potential of Sequential Boost Plans in VMAT Through Simultaneous Optimization. International Journal of Radiation Oncology*Biology*Physics. 108. e381. 10.1016/j.ijrobp.2020.07.2403.

2022

Single-timepoint Low-dimensional Characterization and Classification of Acute versus Chronic Multiple Sclerosis Lesions using Machine Learning

Caba, Bastien & Cafaro, Alexandre & Lombard, Aurélien & Arnold, Douglas & Elliott, Colm & Liu, Dawei & Jiang, Xiaotong & Gafson, Arie & Fisher, Elizabeth & Belachew, Shibeshih & Paragios, Nikos. (2022). Single-timepoint Low-dimensional Characterization and Classification of Acute versus Chronic Multiple Sclerosis Lesions using Machine Learning. NeuroImage. 265. 119787. 10.1016/j.neuroimage.2022.119787.

2022

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

Henry, Théophraste & Sun, Roger & Lerousseau, Marvin & Estienne, Théo & Robert, Charlotte & Besse, Benjamin & Robert, Caroline & Paragios, Nikos & Deutsch, Eric. (2022). Radiomics analysis and metastatic patients: can we really continue to sidestep intra-patient tumor heterogeneity ?. 10.21203/rs.3.rs-1775619/v1.

Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies

Henry, Théophraste & Sun, Roger & Lerousseau, Marvin & Estienne, Théo & Robert, Charline & Besse, B. & Paragios, N. & Deutsch, E.. (2022). Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies. Scientific Reports. 12. 10.1038/s41598-022-20931-z.

2021

Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives

Dercle, L. & Henry, T. & Carré, A. & Paragios, N. & Deutsch, E. & Robert, C. (2021). Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives. Methods, 188, 44-60, 10.1016/j.ymeth.2020.07.003

2020

Radiomics to predict outcomes and abscopal response of patients with cancer treated with immunotherapy combined with radiotherapy using a validated signature of CD8 cells

Sun, Roger & Sundahl, Nora & Hecht, Markus & Putz, Florian & Lancia, Andrea & Rouyar, Angela & Milic, Marina & Carre, Alexandre & Battistella, Enzo & Andres, Emilie & Niyoteka, Stéphane & Romano, Edouard & Louvel, G. & Durand-Labrunie, Jérôme & Bockel, Sophie & Bahleda, Rastislav & Robert, Charlotte & Boutros, Celine & Vakalopoulou, Maria & Deutsch, Eric. (2020). Radiomics to predict outcomes and abscopal response of patients with cancer treated with immunotherapy combined with radiotherapy using a validated signature of CD8 cells. Journal for ImmunoTherapy of Cancer. 8. e001429. 10.1136/jitc-2020-001429.

Radiomics for selection of patients treated with immuno-radiotherapy: pooled analysis from 6 studies

Sun, Roger & Sundahl, Nora & Hecht, Markus & Putz, Florian & Lancia, Andrea & Milic, M. & Carre, Alexandre & Lerousseau, Marvin & Theo, E. & Battistella, Enzo & Andres, E. & Louvel, G. & Durand-Labrunie, Jérôme & Bockel, S. & Bahleda, R. & Robert, Charline & Boutros, Celine & Vakalopoulou, M. & Paragios, Nikos & Deutsch, E.. (2020). PD-0425: Radiomics for selection of patients treated with immuno-radiotherapy: pooled analysis from 6 studies. Radiotherapy and Oncology. 152. S231-S232. 10.1016/S0167-8140(21)00447-3.

Quantification of Cystic Fibrosis Lung Disease with Radiomics-based CT Scores

Chassagnon, Guillaume & Bommart, Sébastien & Burgel, Pierre-Régis & Chiron, Raphael & Dangeard, Severine & Paragios, Nikos & Martin, Clémence & Revel, Marie-Pierre. (2020). Quantification of Cystic Fibrosis Lung Disease with Radiomics-based CT Scores. Radiology: Cardiothoracic Imaging. 2. e200022. 10.1148/ryct.2020200022.

2019

Radiomics to predict response to immunotherapy, bridging the gap from proof of concept to clinical applicability?

Deutsch, E & Paragios, N. (2019). Radiomics to predict response to immunotherapy, bridging the gap from proof of concept to clinical applicability?. Annals of oncology : official journal of the European Society for Medical Oncology. 30. 879-881. 10.1093/annonc/mdz150.

Evaluation of a radiomic signature of CD8 cells in patients treated with immunotherapy-radiotherapy in three clinical trials

Sun, Roger & Lancia, Andrea & Sundahl, Nora & Milic, M. & Carre, Alexandre & Lerousseau, Marvin & Estienne, Théo & Battistella, Enzo & Klausner, Guillaume & Bahleda, R. & Alvarez-Andres, E. & Robert, Charline & Boutros, Celine & Vakalopoulou, M. & Paragios, Nikos & Ost, Piet & Massard, C. & Deutsch, E.. (2019). Evaluation of a radiomic signature of CD8 cells in patients treated with immunotherapy-radiotherapy in three clinical trials. Annals of Oncology. 30. v43. 10.1093/annonc/mdz239.047.

2023

Cosmetic assessment in the UNICANCER HypoG-01 trial: a deep learning approach

Alexandre Cafaro; Amandine Ruffier; Gabriele Bielinyte; Y. Kirova; S. Racadot; M. Benchalal; JB. Clavier; C. Charra-Brunaud;  ME. Chand-Fouche; D. Argo-Leignel; K. Peignaux; A. Benyoucef; D. Pasquier; P. Guilbert; J. Blanchecotte; A. Tallet; A. Petit; G. Bernadou; X. Zasadny; C. Lemanski; J. Fourquet;  E. Malaurie;  H. Kouto; C. Massabeau; A. Henni; Regnault; A. Belliere; Y. Belkacemi; M. Le Blanc-Onfroy; J. Geffrelot; JB. Prevost; E. Karamouza; Stefan Michiels, Marie Bergeaud,  Assia Lamrani-Ghaouti, Sami Rhomdani, Alexis Bombezin–Domino, Nikos Paragios, Sofia Rivera. 2023. Cosmetic assessment in the UNICANCER HypoG-01 trial: a deep learning approach. SABCS

Style-based generative model to reconstruct head and neck 3D CTs

Cafaro, Alexandre & Henry, Théophraste & Spinat, Quentin & Colnot, Julie & Leroy, Amaury & Maury, Pauline & Munoz, Alexandre & Beldjoudi, Guillaume & Hardy, Léo & Robert, Charlotte & Lepetit, Vincent & Paragios, Nikos & Grégoire, Vincent & Deutsch, Eric. 2023. Style-based generative model to reconstruct head and neck 3D CTs. ESTRO

Full 3D CT reconstruction from partial bi-planar projections using a deep generative model

Cafaro, Alexandre & Henry, Théophraste & Colnot, Julie & Spinat, Quentin & Leroy, Amaury & Maury, Pauline & Munoz, Alexandre & Beldjoudi, Guillaume & Oumani, Ayoub & Chabert, Isabelle & Hardy, Léo & Marini Silva, Rafael & Robert, Charlotte & Lepetit, Vincent & Paragios, Nikos & Deutsch, Eric & Grégoire, Vincent. 2023. Full 3D CT reconstruction from partial bi-planar projections using a deep generative model. ESTRO

2022

Region-Guided CycleGANs for Stain Transfer in Whole Slide Images

Boyd, Joseph & Villa, Irène & Mathieu, Marie-Christine & Deutsch, Eric & Paragios, Nikos & Vakalopoulou, Maria & Christodoulidis, Stergios. (2022). Region-Guided CycleGANs for Stain Transfer in Whole Slide Images. 10.1007/978-3-031-16434-7_35.

Joint Deformable Image Registration and ADC Map Regularization: Application to DWI-Based Lymphoma Classification

Kornaropoulos, Evgenios & Zacharaki, Eva & Paragios, Nikos. (2022). Joint Deformable Image Registration and ADC Map Regularization: Application to DWI-Based Lymphoma Classification. IEEE Journal of Biomedical and Health Informatics. PP. 1-1. 10.1109/JBHI.2022.3156009.

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

Leroy, Amaury & Lerousseau, Marvin & Henry, Théophraste & Cafaro, Alexandre & Paragios, Nikos & Grégoire, Vincent & Deutsch, Eric. (2022). End-to-End Multi-Slice-to-Volume Concurrent Registration and Multimodal Generation. 10.1007/978-3-031-16446-0_15.

An efficient training approach for brain paediatrics synthetic CT generation for protontherapy

de Kermenguy, François & Andres, E. & De marzi, Ludovic & Fidon, Lucas & Carre, Alexandre & Bolle, S. & Paragios, N. & Deutsch, E. & Ammari, S. & Robert, Charline. (2022). PO-1621 An efficient training approach for brain paediatrics synthetic CT generation for protontherapy. Radiotherapy and Oncology. 170. S1410-S1411. 10.1016/S0167-8140(22)03585-X.

2021

COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures

Battistella, Enzo & Vakalopoulou, Maria & Sun, Roger & Estienne, Théo & Lerousseau, Marvin & Nikolaev, Sergey & Andres, Emilie & Carre, Alexandre & Niyoteka, Stephane & Robert, Charlotte & Paragios, Nikos & Deutsch, Eric. (2021). COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures. IEEE/ACM Transactions on Computational Biology and Bioinformatics. PP. 1-1. 10.1109/TCBB.2021.3123910.

Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology

Boyd, Joseph & Liashuha, Mykola & Deutsch, Eric & Paragios, Nikos & Christodoulidis, Stergios & Vakalopoulou, Maria. (2021). Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology.

Multimodal Brain Tumor Classification

Lerousseau, Marvin & Deutsch, Eric & Paragios, Nikos. (2021). Multimodal Brain Tumor Classification. 10.1007/978-3-030-72087-2_42.

Magnetic Resonance Imaging Virtual Histopathology from Weakly Paired Data

Leroy, A. & Shreshtha, K. & Lerousseau, M. & Henry, T. & Estienne, T. & Classe, M. & Paragios, N. & Grégoire, V & Deutsch, E.. (2021). Magnetic Resonance Imaging Virtual Histopathology from Weakly Paired Data. Proceedings of the MICCAI Workshop on Computational Pathology . 156:140-150

Holistic artificial intelligence-driven predictor in HER2-positive (HER2+) early breast cancer (BC) treated with neoadjuvant lapatinib and trastuzumab without chemotherapy: A correlative analysis from SOLTI-1114 PAMELA

Battistella, Enzo & Paré Brunet, Laia & Sahasrabudhe, Mihir & Pascual, Tomás & Vakalopoulou, Maria & Villagrasa, Patricia & Deutsch, Eric & Chic, Nuria & Villacampa Javierre, Guillermo & Nuciforo, Paolo & Cortes, Javier & Llombart-Cussac, Antonio & Paragios, Nikos & Prat, Aleix. (2021). Abstract PS5-13: Holistic artificial intelligence-driven predictor in HER2-positive (HER2+) early breast cancer (BC) treated with neoadjuvant lapatinib and trastuzumab without chemotherapy: A correlative analysis from SOLTI-1114 PAMELA. Cancer Research. 81. PS5-13. 10.1158/1538-7445.SABCS20-PS5-13.

Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment

Laousy, Othmane & Chassagnon, Guillaume & Oyallon, Edouard & Paragios, Nikos & Revel, Marie-Pierre & Vakalopoulou, Maria. (2021). Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment. 10.1007/978-3-030-87589-3_33.

Deep Multi-Instance Learning Using Multi-Modal Data for Diagnosis of Lymphocytosis

Sahasrabudhe, Mihir & Sujobert, Pierre & Maurin, Eugénie & Grange, Beatrice & Jallades, Laurent & Paragios, Nikos & Vakalopoulou, Maria. (2020). Deep Multi-Instance Learning Using Multi-Modal Data for Diagnosis of Lymphocytosis. IEEE Journal of Biomedical and Health Informatics. PP. 1-1. 10.1109/JBHI.2020.3038889.

Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?

TN, Hoang & Vakalopoulou, Maria & Christodoulidis, Stergios & Paragios, Nikos & Revel, Marie-Pierre & Chassagnon, Guillaume. (2021). Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?. Diagnostic and interventional imaging. 102. 10.1016/j.diii.2021.10.001.

Deep Learning for Image Matching and Co‐registration

Vakalopoulou, Maria & Christodoulidis, Stergios & Sahasrabudhe, Mihir & Paragios, Nikos. (2021). Deep Learning for Image Matching and Co‐registration. 10.1002/9781119646181.ch9.

Cell-Rad: Towards Histology-driven Radiation Oncology from Multi-Parametric MRI

Leroy, A. & Shreshtha, K. & Lerousseau, Marvin & Henry, Théophraste & Estienne, Théo & Classe, M. & Paragios, Nikos & Deutsch, E. & Grégoire, V.. (2021). OC-0522 Cell-Rad: Towards Histology-driven Radiation Oncology from Multi-Parametric MRI. Radiotherapy and Oncology. 161. S407-S408. 10.1016/S0167-8140(21)06948-6.

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

Henry, Théophraste & Carre, Alexandre & Lerousseau, Marvin & Estienne, Théo & Robert, Charlotte & Paragios, Nikos & Deutsch, Eric. (2021). Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution. 10.1007/978-3-030-72084-1_30.

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia

Chassagnon, Guillaume & Vakalopoulou, Maria & Battistella, Enzo & Christodoulidis, Stergios & TN, Hoang & Dangeard, Severine & Deutsch, Eric & Andre, Fabrice & Guillo, Enora & Halm, Nara & Hajj, Stefany & Bompard, Florian & Neveu, Sophie & Hani, Chahinez & Saab, Ines & Campredon, Aliénor & Koulakian, Hasmik & Bennani, Souhail & Freche, Gael & Paragios, Nikos. (2020). AI-Driven quantification, staging and outcome prediction of COVID-19 pneumonia. Medical Image Analysis. 10.1016/j.media.2020.101860.

2020

Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation

Lerousseau, Marvin & Vakalopoulou, Maria & Classe, Marion & Adam, Julien & Battistella, Enzo & Carre, Alexandre & Estienne, Théo & Henry, Théophraste & Deutsch, Eric & Paragios, Nikos. (2020). Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation. 10.1007/978-3-030-59722-1_45.

Self-supervised Nuclei Segmentation in Histopathological Images Using Attention

Sahasrabudhe, Mihir & Christodoulidis, Stergios & Salgado, Roberto & Michiels, Stefan & Loi, Sherene & Andre, Fabrice & Paragios, Nikos & Vakalopoulou, Maria. (2020). Self-supervised Nuclei Segmentation in Histopathological Images Using Attention. 10.1007/978-3-030-59722-1_38.

Deep Learning–based Approach for Automated Assessment of Interstitial Lung Disease in Systemic Sclerosis on CT Images

Chassagnon, Guillaume & Vakalopoulou, Maria & Regent, Alexis & Aviram, Galit & Martin, Charlotte & Marini, Rafael & Bus, Norbert & Jerjir, Naïm & Arsene, Mekinian & Hua-Huy, Thong & Monnier-Cholley, Laurence & Benmostefa, Nouria & Mouthon, Luc & Dinh-Xuan, Anh Tuan & Paragios, Nikos & Revel, Marie-Pierre. (2020). Deep Learning–based Approach for Automated Assessment of Interstitial Lung Disease in Systemic Sclerosis on CT Images. Radiology: Artificial Intelligence. 2. e190006. 10.1148/ryai.2020190006.

Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation

Estienne, Théo & Lerousseau, Marvin & Vakalopoulou, Maria & Andres, Emilie & Battistella, Enzo & Carre, Alexandre & Chandra, Siddhartha & Christodoulidis, Stergios & Sahasrabudhe, Mihir & Sun, Roger & Robert, Charlotte & Talbot, Hugues & Paragios, Nikos & Deutsch, Eric. (2020). Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation. Frontiers in Computational Neuroscience. 14. 10.3389/fncom.2020.00017.

Artificial intelligence applications for thoracic imaging

Chassagnon, Guillaume & Vakalopoulou, Maria & Paragios, Nikos & Revel, Marie-Pierre. (2019). Artificial Intelligence Applications for Thoracic imaging. European Journal of Radiology. 123. 108774. 10.1016/j.ejrad.2019.108774.

Are current margins in locally advanced cervical cancers treated by tomotherapy appropriate?

Niyoteka, S. & Achkar, S. & Coric, I. & Bourdais, R. & Manea, E. & Dumas, I. & Marini-Silva, R. & Ullmann, E. & Carre, Alexandre & Paragios, Nikos & Deutsch, E. & Chargari, Cyrus & Robert, Charline. (2020). PO-1667: Are current margins in locally advanced cervical cancers treated by tomotherapy appropriate?. Radiotherapy and Oncology. 152. S915-S916. 10.1016/S0167-8140(21)01685-6.

2019

Use of Elastic Registration in Pulmonary MRI for the Assessment of Pulmonary Fibrosis in Patients with Systemic Sclerosis

Chassagnon, Guillaume & Martin, Charlotte & Marini, Rafael & Vakalopolou, Maria & Regent, Alexis & Mouthon, Luc & Paragios, Nikos & Revel, Marie-Pierre. (2019). Use of Elastic Registration in Pulmonary MRI for the Assessment of Pulmonary Fibrosis in Patients with Systemic Sclerosis. Radiology. 291. 182099. 10.1148/radiol.2019182099.

U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets

Estienne, Théo & Vakalopoulou, Maria & Christodoulidis, Stergios & Battistella, Enzo & Lerousseau, Marvin & Carre, Alexandre & Klausner, Guillaume & Sun, Roger & Robert, Charlotte & Mougiakakou, Stavroula & Paragios, Nikos & Deutsch, Eric. (2019). U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets. 10.1007/978-3-030-32248-9_35.

Tighter continuous relaxations for MAP inference in discrete MRFs: A survey

Kannan, Hariprasad & Komodakis, Nikos & Paragios, Nikos. (2019). Tighter continuous relaxations for MAP inference in discrete MRFs: A survey. 10.1016/bs.hna.2019.06.001.

Gene Expression High-Dimensional Clustering Towards a Novel, Robust, Clinically Relevant and Highly Compact Cancer Signature

Battistella, Enzo & Vakalopoulou, Maria & Estienne, Théo & Lerousseau, Marvin & Sun, Roger & Robert, Charlotte & Paragios, Nikos & Deutsch, Eric. (2019). Gene Expression High-Dimensional Clustering Towards a Novel, Robust, Clinically Relevant and Highly Compact Cancer Signature. 10.1007/978-3-030-17938-0_41.

Deep learning: definition and perspectives for thoracic imaging

Chassagnon, Guillaume & Vakalopolou, Maria & Paragios, Nikos & Revel, Marie-Pierre. (2019). Deep learning: definition and perspectives for thoracic imaging. European Radiology. 30. 10.1007/s00330-019-06564-3.

Context Aware 3D CNNs for Brain Tumor Segmentation

Chandra, S. & Vakalopoulou, M. & Fidon, L. & Battistella, E. & Estienne, T. & Sun, R. & Robert, C. & Deutsch, E. & Paragios, N. (2019). Context Aware 3D CNNs for Brain Tumor Segmentation. Lecture Notes in Computer Science, 11384, 10.1007/978-3-030-11726-9_27