pr\u00e9sentent<\/p>\n\n\n\n
Autoris\u00e9 \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 l’Institut Polytechnique de Paris T\u00e9l\u00e9com SudParis en :<\/p>\n\n\n\n
le JEUDI 3 D\u00e9CEMBRE 2026 \u00e0 14h00<\/p>\n\n\n\n
\u00e0<\/p>\n\n\n\n
Salle C06
9 rue Charles Fourier, 91000, Evry-Courcouronnes<\/p>\n\n\n\n
Membres du jury :<\/strong><\/p>\n\n\n\n M. Catalin FETITA<\/strong>, Professeur, Institut Polytechnique de Paris, T\u00e9l\u00e9com SudParis, FRANCE – Directeur de th\u00e8se Invit\u00e9 :<\/strong><\/p>\n\n\n\n M. Radu ISPAS<\/strong>, Docteur, Keyrus<\/p>\n\n\n\n R\u00e9sum\u00e9 :<\/strong><\/p>\n\n\n\n Les pneumopathies interstitielles diffuses (PID), incluant la fibrose pulmonaire idiopathique (FPI) et la pneumopathie interstitielle non sp\u00e9cifique (PINS), provoquent une d\u00e9gradation continue et irr\u00e9versible de la fonction pulmonaire et n\u00e9cessitent un suivi quantitatif r\u00e9gulier par tomodensitom\u00e9trie haute r\u00e9solution (TDM-HR). La construction d\u2019outils fiables d\u2019apprentissage profond pour ce suivi se heurte \u00e0 deux obstacles structurels : la raret\u00e9 chronique des donn\u00e9es pathologiques annot\u00e9es, les cas de PID \u00e9tant plus rares et plus h\u00e9t\u00e9rog\u00e8nes que les examens sains, et la n\u00e9cessit\u00e9 d\u2019un niveau de confiance dans les pr\u00e9dictions \u00e0 la hauteur d\u2019un suivi clinique longitudinal, en particulier pour les motifs pathologiques rares ou sous-repr\u00e9sent\u00e9s. Cette th\u00e8se, r\u00e9alis\u00e9e dans le cadre d\u2019une convention CIFRE entre le laboratoire SAMOVAR de T\u00e9l\u00e9com SudParis, Institut Polytechnique de Paris, et le partenaire industriel Keyrus, r\u00e9pond \u00e0 ces deux obstacles au moyen d\u2019un pipeline unique, structur\u00e9 anatomiquement, analysant le poumon \u00e0 quatre niveaux coupl\u00e9s de granularit\u00e9 croissante, associant mod\u00e9lisation g\u00e9n\u00e9rative (GAN, mod\u00e8les de diffusion) et architectures sensibles \u00e0 la topologie et \u00e0 la g\u00e9om\u00e9trie (r\u00e9seaux de graphes et de nuages de points). La premi\u00e8re contribution r\u00e9alise une segmentation de la forme globale du poumon robuste \u00e0 une pathologie interstitielle s\u00e9v\u00e8re, par augmentation de donn\u00e9es synth\u00e9tiques fond\u00e9e sur CycleGAN. La deuxi\u00e8me reformule le partitionnement lobaire comme une classification de nuages de points vasculaires via PointNet++, \u00e9tendue \u00e0 l\u2019UTE-MRI. La troisi\u00e8me aborde la segmentation texturale du parenchyme \u00e0 travers deux strat\u00e9gies fond\u00e9es sur la diffusion, DiffLung (supervis\u00e9e) et DiffSegLung (non supervis\u00e9e, par distillation radiomique). La quatri\u00e8me am\u00e9liore la segmentation de l\u2019arbre bronchique par un r\u00e9seau d\u2019attention sur graphes (HR-NET-GAT) op\u00e9rant sur le squelette des voies a\u00e9riennes. Valid\u00e9es sur des cohortes r\u00e9elles, notamment 156 patients atteints de PID de l\u2019h\u00f4pital Avicenne, les quatre contributions forment un pipeline coh\u00e9rent r\u00e9pondant \u00e0 la raret\u00e9 des donn\u00e9es par la synth\u00e8se g\u00e9n\u00e9rative et \u00e0 la n\u00e9cessit\u00e9 de fiabilit\u00e9 des pr\u00e9dictions par une mod\u00e9lisation sensible \u00e0 la topologie et non supervis\u00e9e. Interstitial lung diseases (ILDs), including idiopathic pulmonary fibrosis (IPF) and nonspecific interstitial pneumonia (NSIP), cause continuous and irreversible decline in lung function and require regular quantitative monitoring through high-resolution computed tomography (HRCT). Building reliable deep learning tools for this monitoring is made difficult by two structural obstacles: the chronic scarcity of annotated pathological data, since ILD cases are rarer and more heterogeneous than healthy scans, and the need for prediction confidence commensurate with longitudinal clinical follow-up, particularly for rare or under-represented disease patterns. This thesis, conducted under a CIFRE convention between the SAMOVAR laboratory at T\u00e9l\u00e9com SudParis, Institut Polytechnique de Paris, and the industrial partner Keyrus, addresses both obstacles through a single anatomically structured pipeline analyzing the lung at four coupled levels of increasing detail, combining generative modeling (GANs, diffusion models) with topology- and geometry-aware architectures (graph and point cloud networks). The first contribution performs whole-lung shape segmentation robust to severe ILD pathology using CycleGAN-based synthetic data augmentation. The second reformulates lobar partitioning as vascular point cloud classification with PointNet++, extended to UTE-MRI. The third addresses parenchymal texture segmentation through two diffusion-based strategies, DiffLung (supervised) and DiffSegLung (unsupervised, via radiomic distillation). The fourth improves airway tree segmentation through a graph attention network (HR-NET-GAT) operating on the airway skeleton. Validated on real cohorts, notably 156 ILD patients from Avicenne Hospital, the four contributions form a coherent pipeline addressing data scarcity through generative synthesis and prediction confidence through topology-aware and unsupervised modeling.<\/p>\n","protected":false},"excerpt":{"rendered":" L’Ecole doctorale : Ecole Doctorale de l’Institut Polytechnique de Paris et le Laboratoire de recherche SAMOVAR – Services r\u00e9partis, Architectures, Mod\u00e9lisation, Validation, Administration des R\u00e9seaux pr\u00e9sentent l\u2019AVIS DE SOUTENANCE de Monsieur Rezkellah Nour Eddine KHIATI Autoris\u00e9 \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 l’Institut Polytechnique 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Mme Yasmina CHENOUNE<\/strong>, Maitresse de conf\u00e9rences, ESME \u00c9cole Sp\u00e9ciale de M\u00e9canique et d’Electricit\u00e9, FRANCE – Rapporteur
M. Nicolas LOM\u00e9NIE<\/strong>, Professeur, Universit\u00e9 Paris Cit\u00e9, FRANCE – Rapporteur
Mme Elsa ANGELINI<\/strong>, Professeure, Telecom Paris, FRANCE – Examinateur
M. Gr\u00e9goire MALANDAIN<\/strong>, Directeur de recherche, INRIA Sophia-Antipolis, FRANCE – Examinateur
M. Gael DOURNES<\/strong>, Professeur des universit\u00e9s – praticien hospitalier, Universit\u00e9 de Bordeaux, FRANCE – Examinateur
M. Pierre-Yves BRILLET<\/strong>, Professeur des universit\u00e9s – praticien hospitalier, Universit\u00e9 Sorbonne Paris Nord, FRANCE – Examinateur
Mme Ruxandra TAPU<\/strong>, Maitresse de conf\u00e9rences , Institut Polytechnique de Paris, T\u00e9l\u00e9com SudParis, FRANCE – Co-encadrant de th\u00e8se<\/p>\n\n\n\n\u00ab Apprentissage profond confiant pour l’analyse pr\u00e9dictive et le suivi des maladies pulmonaires interstitielles. \u00bb<\/h2>\n\n\n\n
pr\u00e9sent\u00e9 par Monsieur Rezkellah Nour Eddine KHIATI<\/h2>\n\n\n\n
Abstract :<\/strong><\/p>\n\n\n\n