Publications of year 2025

1. Marco Lorenzi and Maria A. Zuluaga. Trustworthy AI in Medical Imaging. Elsevier, 2025.
   

   @book{lorenzi:hal-04952319,
   TITLE = {{Trustworthy AI in Medical Imaging}},
   AUTHOR = {Lorenzi, Marco and Zuluaga, Maria A.},
   url-hal= {https://hal.science/hal-04952319},
   PUBLISHER = {{Elsevier}},
   YEAR = {2025},
   DOI = {10.1016/C2023-0-00875-5},
   HAL_ID = {hal-04952319},
   HAL_VERSION = {v1},
   
   }
   

1. Sébastien Frey, Federica Facente, Wen Wei, Ezem Sura Ekmekci, Eric Séjor, Patrick Baqué, Matthieu Durand, Hervé Delingette, Francois Bremond, Pierre Berthet-Rayne, and Nicholas Ayache. Optimizing Intraoperative AI: Evaluation of YOLOv8 for Real-Time Recognition of Robotic and Laparoscopic Instruments. Journal of Robotic Surgery, 19(131), March 2025. Keyword(s): Computer-Assisted Surgery, Computer vision, Surgical instrument detection, Instrument segmentation, Robotic surgery, YoloV8.
   

   @article{frey:hal-04845670,
   TITLE = {{Optimizing Intraoperative AI: Evaluation of YOLOv8 for Real-Time Recognition of Robotic and Laparoscopic Instruments}},
   AUTHOR = {Frey, S{\'e}bastien and Facente, Federica and Wei, Wen and Ekmekci, Ezem Sura and S{\'e}jor, Eric and Baqu{\'e}, Patrick and Durand, Matthieu and Delingette, Herv{\'e} and Bremond, Francois and Berthet-Rayne, Pierre and Ayache, Nicholas},
   url-hal= {https://hal.science/hal-04845670},
   JOURNAL = {{Journal of Robotic Surgery}},
   PUBLISHER = {{Springer Verlag}},
   VOLUME = {19},
   NUMBER = {131},
   YEAR = {2025},
   MONTH = Mar,
   DOI = {10.1007/s11701-025-02284-7},
   KEYWORDS = {Computer-Assisted Surgery ; Computer vision ; Surgical instrument detection ; Instrument segmentation ; Robotic surgery ; YoloV8},
   HAL_ID = {hal-04845670},
   HAL_VERSION = {v1},
   
   }
   




2. Sebastien Goffart, Hervé Delingette, Andrea Chierici, Lisa Guzzi, Bahaa Nasr, Fabien Lareyre, and Juliette Raffort. Artificial Intelligence Techniques for Prognostic and Diagnostic Assessments in Peripheral Artery Disease: A Scoping Review. Angiology, 2025. Keyword(s): Machine Learning, Peripheral Artery Disease (PAD), Artificial Intelligence (AI), Machine Learning (ML), Diagnosis, Prognosis.
   

   @article{goffart:hal-04922713,
   TITLE = {{Artificial Intelligence Techniques for Prognostic and Diagnostic Assessments in Peripheral Artery Disease: A Scoping Review}},
   AUTHOR = {Goffart, Sebastien and Delingette, Herv{\'e} and Chierici, Andrea and Guzzi, Lisa and Nasr, Bahaa and Lareyre, Fabien and Raffort, Juliette},
   url-hal= {https://hal.science/hal-04922713},
   JOURNAL = {{Angiology}},
   PUBLISHER = {{SAGE Publications}},
   YEAR = {2025},
   DOI = {10.1177/00033197241310572},
   KEYWORDS = {Machine Learning ; Peripheral Artery Disease (PAD) ; Artificial Intelligence (AI) ; Machine Learning (ML) ; Diagnosis ; Prognosis},
   PDF = {https://hal.science/hal-04922713v1/file/Manuscript_final.pdf},
   HAL_ID = {hal-04922713},
   HAL_VERSION = {v1},
   
   }
   




3. Sébastien Molière, Dimitri Hamzaoui, Guillaume Ploussard, Romain Mathieu, Gaëlle Fiard, Michael Baboudjian, benjamin granger, Morgan Roupret, Hervé Delingette, and Raphaële Renard-Penna. A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging. European Urology Oncology, 2025. Keyword(s): Artificial intelligence, Deep learning, Diagnostic accuracy, Magnetic resonance imaging, Prostate cancer, Systematic review.
   

   @article{moliere:hal-04827079,
   TITLE = {{A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging}},
   AUTHOR = {Moli{\`e}re, S{\'e}bastien and Hamzaoui, Dimitri and Ploussard, Guillaume and Mathieu, Romain and Fiard, Ga{\"e}lle and Baboudjian, Michael and granger, benjamin and Roupret, Morgan and Delingette, Herv{\'e} and Renard-Penna, Rapha{\"e}le},
   url-hal= {https://hal.science/hal-04827079},
   JOURNAL = {{European Urology Oncology}},
   PUBLISHER = {{Elsevier}},
   YEAR = {2025},
   DOI = {10.1016/j.euo.2024.11.001},
   KEYWORDS = {Artificial intelligence ; Deep learning ; Diagnostic accuracy ; Magnetic resonance imaging ; Prostate cancer ; Systematic review},
   PDF = {https://hal.science/hal-04827079v1/file/1-s2.0-S2588931124002487-main.pdf},
   HAL_ID = {hal-04827079},
   HAL_VERSION = {v1},
   
   }
   




4. Wilhelm Wimmer and Hervé Delingette. Second order kinematic surface fitting in anatomical structures. Medical Image Analysis, pp 103488, February 2025. Keyword(s): Stationary velocity field, Symmetry, Core line detection, Cochlea, Shape classification.
   

   @article{wimmer:hal-04937619,
   TITLE = {{Second order kinematic surface fitting in anatomical structures}},
   AUTHOR = {Wimmer, Wilhelm and Delingette, Herv{\'e}},
   url-hal= {https://inria.hal.science/hal-04937619},
   JOURNAL = {{Medical Image Analysis}},
   PUBLISHER = {{Elsevier}},
   PAGES = {103488},
   YEAR = {2025},
   MONTH = Feb,
   DOI = {10.1016/j.media.2025.103488},
   KEYWORDS = {Stationary velocity field ; Symmetry ; Core line detection ; Cochlea ; Shape classification},
   PDF = {https://inria.hal.science/hal-04937619v1/file/Final___Second_Order_Kinematic_Surface_Fitting_in_Anatomical_Structures%20%281%29.pdf},
   HAL_ID = {hal-04937619},
   HAL_VERSION = {v1},
   
   }
   




5. Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Riccardo Taiello, Santiago Silva, Samy-Safwan Ayed, Melek Önen, Fanny Orlhac, Christophe Nioche, Bastien Houis, Romain Modzelewski, Nathan Lapel, Renaud Schiappa, Olivier Humbert, and Marco Lorenzi. Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. In Federated Learning Systems, volume 832 of Studies in Computational Intelligence, pages 19-41. Springer Nature Switzerland, April 2025. Keyword(s): Machine learning, Biomedical Application, Healthcare, Federated Learning Framework.
   

   @incollection{cremonesi:hal-04081557,
   TITLE = {{Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications}},
   AUTHOR = {Cremonesi, Francesco and Vesin, Marc and Cansiz, Sergen and Bouillard, Yannick and Balelli, Irene and Innocenti, Lucia and Taiello, Riccardo and Silva, Santiago and Ayed, Samy-Safwan and {\"O}nen, Melek and Orlhac, Fanny and Nioche, Christophe and Houis, Bastien and Modzelewski, Romain and Lapel, Nathan and Schiappa, Renaud and Humbert, Olivier and Lorenzi, Marco},
   url-hal= {https://inria.hal.science/hal-04081557},
   BOOKTITLE = {{Federated Learning Systems}},
   PUBLISHER = {{Springer Nature Switzerland}},
   SERIES = {Studies in Computational Intelligence},
   VOLUME = {832},
   PAGES = {19-41},
   YEAR = {2025},
   MONTH = Apr,
   DOI = {10.1007/978-3-031-78841-3\_2},
   KEYWORDS = {Machine learning ; Biomedical Application ; Healthcare ; Federated Learning Framework},
   PDF = {https://inria.hal.science/hal-04081557v1/file/fedbiomed_paper-5.pdf},
   HAL_ID = {hal-04081557},
   HAL_VERSION = {v1},
   
   }
   

1. Safaa Al-Ali, Jairo Rodrìguez Padilla, Maxime Sermesant, and Irene Balelli. Cardiac Electromechanical Model Sensitivity Analysis using Causal Discovery. In Lecture Notes in Computer Science, volume 15672 of Lecture Notes in Computer Science, Dallas, United States, pages 343-355, June 2025. Radomìr Chabiniok, Springer Nature Switzerland. Keyword(s): Heart modeling, Electromechanical properties, Causal discovery, Sensitivity analysis.
   

   @inproceedings{alali:hal-05121540,
   TITLE = {{Cardiac Electromechanical Model Sensitivity Analysis using Causal Discovery}},
   AUTHOR = {Al-Ali, Safaa and Padilla, Jairo Rodr{\'i}guez and Sermesant, Maxime and Balelli, Irene},
   url-hal= {https://hal.science/hal-05121540},
   BOOKTITLE = {{Lecture Notes in Computer Science}},
   ADDRESS = {Dallas, United States},
   ORGANIZATION = {{Radom{\'i}r Chabiniok}},
   PUBLISHER = {{Springer Nature Switzerland}},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {15672},
   PAGES = {343-355},
   YEAR = {2025},
   MONTH = Jun,
   DOI = {10.1007/978-3-031-94559-5\_31},
   KEYWORDS = {Heart modeling ; Electromechanical properties ; Causal discovery ; Sensitivity analysis},
   PDF = {https://hal.science/hal-05121540v1/file/Preprint_FIMH_2025.pdf},
   HAL_ID = {hal-05121540},
   HAL_VERSION = {v1},
   
   }
   




2. Camilla Ferrario, Jairo Rodrìguez-Padilla, Maelys Venet, Olivier Villemain, and Maxime Sermesant. Myocardial Stiffness Quantification using Ultrasound Shear Wave Elastography and Reduced Modeling for Subject-specific Simulations. In FIMH 2025 - Functional Imaging and Modeling of the Heart, volume 15673, Dallas (TX), United States, pages 41-54, June 2025. Keyword(s): Cardiac mechanics, reduced-order models, ultrafast ultrasound imaging, myocardial stiffness, patient-specific analyses.
   

   @inproceedings{ferrario:hal-05123170,
   TITLE = {{Myocardial Stiffness Quantification using Ultrasound Shear Wave Elastography and Reduced Modeling for Subject-specific Simulations}},
   AUTHOR = {Ferrario, Camilla and Rodr{\'i}guez-Padilla, Jairo and Venet, Maelys and Villemain, Olivier and Sermesant, Maxime},
   url-hal= {https://hal.science/hal-05123170},
   BOOKTITLE = {{FIMH 2025 - Functional Imaging and Modeling of the Heart}},
   ADDRESS = {Dallas (TX), United States},
   VOLUME = {15673},
   NUMBER = {ISBN 978‑3‑031‑94562‑5},
   PAGES = {41-54},
   YEAR = {2025},
   MONTH = Jun,
   DOI = {10.1007/978-3-031-94562-5\_5},
   KEYWORDS = {Cardiac mechanics ; reduced-order models ; ultrafast ultrasound imaging ; myocardial stiffness ; patient-specific analyses},
   PDF = {https://hal.science/hal-05123170v1/file/FIMH_definitivo_official-3.pdf},
   HAL_ID = {hal-05123170},
   HAL_VERSION = {v1},
   
   }
   




3. Huiyu Li, Nicholas Ayache, and Hervé Delingette. Generative medical image anonymization based on latent code projection and optimization. In IEEE Xplore, Houston (Texas), United States, April 2025. Keyword(s): Medical image anonymization, Identity utility trade-off, Latent code optimization.
   

   @inproceedings{li:hal-04913904,
   TITLE = {{Generative medical image anonymization based on latent code projection and optimization}},
   AUTHOR = {Li, Huiyu and Ayache, Nicholas and Delingette, Herv{\'e}},
   url-hal= {https://inria.hal.science/hal-04913904},
   BOOKTITLE = {{IEEE Xplore}},
   ADDRESS = {Houston (Texas), United States},
   YEAR = {2025},
   MONTH = Apr,
   KEYWORDS = {Medical image anonymization ; Identity utility trade-off ; Latent code optimization},
   PDF = {https://inria.hal.science/hal-04913904v1/file/ISBI%20%283%29.pdf},
   HAL_ID = {hal-04913904},
   HAL_VERSION = {v1},
   
   }
   




4. Buntheng Ly, Nicolas Cedilnik, Mihaela Pop, Josselin Duchateau, Frédéric Sacher, Pierre Jaïs, Hubert Cochet, and Maxime Sermesant. Multimodal Personalisation of Cardiac Electrophysiology Models combining 12-lead ECG and Computed Tomography. In Radomìr Chabiniok, Qing Zou, Tarique Hussain, Hoang H. Nguyen, Vlad G. Zaha, and Maria Gusseva, editors, Lecture Notes in Computer Science, Dallas (TX), United States, June 2025. Radomir Chabiniok and Maria Gusseva and Tarique Hussain and Hoang Nguyen and Vlad Zaha and Qing Zou. Keyword(s): Computational Modelling, Myocardial Infarction, ECG, Computed Tomography, Model Personalisation.
   

   @inproceedings{ly:hal-05122021,
   TITLE = {{Multimodal Personalisation of Cardiac Electrophysiology Models combining 12-lead ECG and Computed Tomography}},
   AUTHOR = {Ly, Buntheng and Cedilnik, Nicolas and Pop, Mihaela and Duchateau, Josselin and Sacher, Fr{\'e}d{\'e}ric and Ja{\"i}s, Pierre and Cochet, Hubert and Sermesant, Maxime},
   url-hal= {https://inria.hal.science/hal-05122021},
   BOOKTITLE = {{Lecture Notes in Computer Science}},
   ADDRESS = {Dallas (TX), United States},
   ORGANIZATION = {{Radomir Chabiniok and Maria Gusseva and Tarique Hussain and Hoang Nguyen and Vlad Zaha and Qing Zou}},
   EDITOR = {Radom{\'i}r Chabiniok and Qing Zou and Tarique Hussain and Hoang H. Nguyen and Vlad G. Zaha and Maria Gusseva},
   YEAR = {2025},
   MONTH = Jun,
   KEYWORDS = {Computational Modelling ; Myocardial Infarction ; ECG ; Computed Tomography ; Model Personalisation},
   PDF = {https://inria.hal.science/hal-05122021v1/file/fimh2025_bunthengly_finalised.pdf},
   HAL_ID = {hal-05122021},
   HAL_VERSION = {v1},
   
   }
   




5. Maëlis Morier, Jairo Rodrìguez-Padilla, Maxime Sermesant, and Patrick Gallinari. Learning Cardiac Electrophysiology with Graph Neural Networks for Fast Data-driven Personalised Predictions. In FIMH 2025 - 13th Functional Imaging and Modeling of the Heart International Conference, volume 15672 (Part I) and 15673 (Part II), Dallas (TX), United States, pages LNCS 15672 and LNCS 15673, June 2025. Radomìr Chabiniok. Keyword(s): Graph Neural Network, Data-Driven Modeling, Personalized Cardiac Electrophysiology, Graph Neural Network Data-Driven Modeling Personalized Cardiac Electrophysiology, Graph Neural Network.
   

   @inproceedings{morier:hal-05114524,
   TITLE = {{Learning Cardiac Electrophysiology with Graph Neural Networks for Fast Data-driven Personalised Predictions}},
   AUTHOR = {Morier, Ma{\"e}lis and Rodr{\'i}guez-Padilla, Jairo and Sermesant, Maxime and Gallinari, Patrick},
   url-hal= {https://hal.science/hal-05114524},
   BOOKTITLE = {{FIMH 2025 - 13th Functional Imaging and Modeling of the Heart International Conference}},
   ADDRESS = {Dallas (TX), United States},
   ORGANIZATION = {{Radom{\'i}r Chabiniok}},
   VOLUME = {15672 (Part I) and 15673 (Part II)},
   PAGES = {LNCS 15672 and LNCS 15673},
   YEAR = {2025},
   MONTH = Jun,
   KEYWORDS = {Graph Neural Network ; Data-Driven Modeling ; Personalized Cardiac Electrophysiology ; Graph Neural Network Data-Driven Modeling Personalized Cardiac Electrophysiology ; Graph Neural Network},
   PDF = {https://hal.science/hal-05114524v2/file/AGATA_hal.pdf},
   HAL_ID = {hal-05114524},
   HAL_VERSION = {v2},
   
   }
   




6. Jairo Rodrìguez Padilla, Rafael Silva, Buntheng Ly, Graham Wright, Mihaela Pop, and Maxime Sermesant. In silico Assessment of Arrhythmia Inducibility Dependence on Stimulus Location using Calibrated MR-based Infarcted Heart Models. In FIMH 2025 - 13th Functional Imaging and Modeling of the Heart International Conference, Dallas, United States, June 2025. Keyword(s): Electrophysiology, Modeling, Simulations, Finite Element, Personalization.
   

   @inproceedings{padilla:hal-05119965,
   TITLE = {{In silico Assessment of Arrhythmia Inducibility Dependence on Stimulus Location using Calibrated MR-based Infarcted Heart Models}},
   AUTHOR = {Padilla, Jairo Rodr{\'i}guez and Silva, Rafael and Ly, Buntheng and Wright, Graham and Pop, Mihaela and Sermesant, Maxime},
   url-hal= {https://inria.hal.science/hal-05119965},
   BOOKTITLE = {{FIMH 2025 - 13th Functional Imaging and Modeling of the Heart International Conference}},
   ADDRESS = {Dallas, United States},
   YEAR = {2025},
   MONTH = Jun,
   KEYWORDS = {Electrophysiology ; Modeling ; Simulations ; Finite Element ; Personalization},
   PDF = {https://inria.hal.science/hal-05119965v1/file/FIMH_2025_halversion.pdf},
   HAL_ID = {hal-05119965},
   HAL_VERSION = {v1},
   
   }
   




7. Riccardo Taiello, Clémentine Gritti, Melek Önen, and Marco Lorenzi. Buffalo: A Practical Secure Aggregation Protocol for Buffered Asynchronous Federated Learning. In CODASPY 2025 - 15th ACM Conference on Data and Application Security and Privacy, Pittsburgh, United States, pages 1-12, June 2025. ACM, ACM. Keyword(s): Secure Aggregation, Asynchronous Federated Learning.
   

   @inproceedings{taiello:hal-05034678,
   TITLE = {{Buffalo: A Practical Secure Aggregation Protocol for Buffered Asynchronous Federated Learning}},
   AUTHOR = {Taiello, Riccardo and Gritti, Cl{\'e}mentine and {\"O}nen, Melek and Lorenzi, Marco},
   url-hal= {https://hal.science/hal-05034678},
   BOOKTITLE = {{CODASPY 2025 - 15th ACM Conference on Data and Application Security and Privacy}},
   ADDRESS = {Pittsburgh, United States},
   ORGANIZATION = {{ACM}},
   PUBLISHER = {{ACM}},
   PAGES = {1-12},
   YEAR = {2025},
   MONTH = Jun,
   DOI = {10.1145/3714393.3726498},
   KEYWORDS = {Secure Aggregation ; Asynchronous Federated Learning},
   PDF = {https://hal.science/hal-05034678v1/file/publi-8173.pdf},
   HAL_ID = {hal-05034678},
   HAL_VERSION = {v1},
   
   }
   

1. Yanis Aeschlimann, Anna Calissano, Théodore Papadopoulo, and Samuel Deslauriers-Gauthier. Brain network alignment using structural and functional connectivity with anatomical constraints. Note: Working paper or preprint, January 2025. Keyword(s): Structural connectivity, Functional connectivity, cortical atlas, graph alignment, inter-subject variability.
   

   @unpublished{aeschlimann:hal-04387986,
   TITLE = {{Brain network alignment using structural and functional connectivity with anatomical constraints}},
   AUTHOR = {Aeschlimann, Yanis and Calissano, Anna and Papadopoulo, Th{\'e}odore and Deslauriers-Gauthier, Samuel},
   url-hal= {https://hal.science/hal-04387986},
   NOTE = {working paper or preprint},
   YEAR = {2025},
   MONTH = Jan,
   KEYWORDS = {Structural connectivity ; Functional connectivity ; cortical atlas ; graph alignment ; inter-subject variability},
   PDF = {https://hal.science/hal-04387986v2/file/ISBI_2025_Yanis_reviewed_version.pdf},
   HAL_ID = {hal-04387986},
   HAL_VERSION = {v2},
   
   }
   




2. Francesco Cremonesi, Lucie Chambon, Nelson Mokkadem, Huyen Thi Trang Nguyen, Oliver Humbert, and Marco Lorenzi. Knowledge-based semantic enrichment of medical imaging data for automatic phenotyping and pattern discovery in metastatic lung cancer. Note: Working paper or preprint, February 2025.
   

   @unpublished{cremonesi:hal-04879690,
   TITLE = {{Knowledge-based semantic enrichment of medical imaging data for automatic phenotyping and pattern discovery in metastatic lung cancer}},
   AUTHOR = {Cremonesi, Francesco and Chambon, Lucie and Mokkadem, Nelson and Nguyen, Huyen Thi Trang and Humbert, Oliver and Lorenzi, Marco},
   url-hal= {https://hal.science/hal-04879690},
   NOTE = {working paper or preprint},
   YEAR = {2025},
   MONTH = Feb,
   PDF = {https://hal.science/hal-04879690v1/file/whitepaper.pdf},
   HAL_ID = {hal-04879690},
   HAL_VERSION = {v1},
   
   }
   




3. Francesco Cremonesi, Lucia Innocenti, Sebastien Ourselin, Vicky Goh, Michela Antonelli, and Marco Lorenzi. A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications. Note: Working paper or preprint, January 2025. Keyword(s): Collaborative learning, healthcare, sustainable AI, trustworthy AI, federated learning consensus-based learning, medical imaging.
   

   @unpublished{cremonesi:hal-04893012,
   TITLE = {{A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications}},
   AUTHOR = {Cremonesi, Francesco and Innocenti, Lucia and Ourselin, Sebastien and Goh, Vicky and Antonelli, Michela and Lorenzi, Marco},
   url-hal= {https://hal.science/hal-04893012},
   NOTE = {working paper or preprint},
   YEAR = {2025},
   MONTH = Jan,
   KEYWORDS = {Collaborative learning ; healthcare ; sustainable AI ; trustworthy AI ; federated learning consensus-based learning ; medical imaging},
   PDF = {https://hal.science/hal-04893012v1/file/2412.06494v1.pdf},
   HAL_ID = {hal-04893012},
   HAL_VERSION = {v1},
   
   }
   




4. Tom Szwagier, Guillaume Olikier, and Xavier Pennec. Eigengap Sparsity for Covariance Parsimony. Note: Working paper or preprint, April 2025. Keyword(s): Covariance estimation, Parsimony, Eigengaps, Flag manifolds, Monotone cone, Isotonic regression.
   

   @unpublished{szwagier:hal-05047196,
   TITLE = {{Eigengap Sparsity for Covariance Parsimony}},
   AUTHOR = {Szwagier, Tom and Olikier, Guillaume and Pennec, Xavier},
   url-hal= {https://inria.hal.science/hal-05047196},
   NOTE = {working paper or preprint},
   YEAR = {2025},
   MONTH = Apr,
   KEYWORDS = {Covariance estimation ; Parsimony ; Eigengaps ; Flag manifolds ; Monotone cone ; Isotonic regression},
   PDF = {https://inria.hal.science/hal-05047196v1/file/ESCP_GSI25.pdf},
   HAL_ID = {hal-05047196},
   HAL_VERSION = {v1},
   
   }
   




5. Tom Szwagier and Xavier Pennec. Nested Subspace Learning with Flags. Note: Working paper or preprint, April 2025. Keyword(s): subspace learning, Grassmann manifolds, flag manifolds, nested subspaces, dimensionality reduction.
   

   @unpublished{szwagier:hal-05047175,
   TITLE = {{Nested Subspace Learning with Flags}},
   AUTHOR = {Szwagier, Tom and Pennec, Xavier},
   url-hal= {https://inria.hal.science/hal-05047175},
   NOTE = {working paper or preprint},
   YEAR = {2025},
   MONTH = Apr,
   KEYWORDS = {subspace learning ; Grassmann manifolds ; flag manifolds ; nested subspaces ; dimensionality reduction},
   PDF = {https://inria.hal.science/hal-05047175v1/file/Flag_Trick.pdf},
   HAL_ID = {hal-05047175},
   HAL_VERSION = {v1},
   
   }
   

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