https:\/\/telecom-paris.zoom.us\/j\/94310229016?pwd=cTBNc2wvSE1TSkpRWVZBNC85WU1MQT09<\/a><\/p>\n\n\n\nMembres du jury :<\/strong><\/p>\n\n\n\nM. Yaneck GOTTESMAN<\/strong>, Professeur, T\u00e9l\u00e9com SudParis, FRANCE – Directeur de these
Mme Bernadette DORRIZZI<\/strong>, Professeure \u00e9m\u00e9rite, T\u00e9l\u00e9com SudParis, FRANCE – Co-encadrant de these
M. JIhad ZALLAT<\/strong>, Professeur, Universit\u00e9 de Strasbourg, FRANCE – Rapporteur
M. Laurent BARTHES<\/strong>, Ma\u00eetre de conf\u00e9rences, Universit\u00e9 de Versailles Saint-Quentin en Yvelines \/ Paris Saclay, FRANCE – Rapporteur
Mme Pauline TROUV\u00c9-PELOUX<\/strong>, Ing\u00e9nieure de recherche, ONERA – the French Aerospace Lab, FRANCE – Examinateur
Mme Elsa ANGELINI<\/strong>, Professeure, T\u00e9l\u00e9com Paris, FRANCE – Examinateur<\/p>\n\n\n\nInvit\u00e9s :<\/strong><\/p>\n\n\n\nM. KLOSSA Jacques<\/strong><\/p>\n\n\n\nM. LEYMARIE Vincent<\/strong><\/p>\n\n\n\n\u00ab Analyse automatique des images des frottis sanguins : int\u00e9r\u00eat de la phase en microscopie ptychographique de Fourier \u00bb<\/h2>\n\n\n\npr\u00e9sent\u00e9 par Madame Houda HASSINI<\/h2>\n\n\n\n
R\u00e9sum\u00e9 :<\/strong><\/p>\n\n\n\nLa pathologie num\u00e9rique constitue aujourd’hui un outil fondamental pour le diagnostic m\u00e9dical, exploitant les avanc\u00e9es technologiques en mati\u00e8re de num\u00e9risation pour transformer les \u00e9chantillons biologiques en donn\u00e9es num\u00e9riques, facilitant ainsi leur visualisation et leur analyse. Cependant, ces m\u00e9thodes, souvent bas\u00e9es sur la microscopie conventionnelle, rencontrent des limitations qui entravent parfois leur efficacit\u00e9. Dans ce contexte, des m\u00e9thodes d’imagerie non conventionnelles telles que la microscopie ptychographique de Fourier (FPM) offrent des perspectives prometteuses pour surmonter ces limitations. En effet, la FPM offre un acc\u00e8s \u00e0 la phase en compl\u00e9ment de l’intensit\u00e9 et permet d’examiner un large champ de vision \u00e0 haute r\u00e9solution \u00e0 un co\u00fbt de conception raisonnable. Cette th\u00e8se explore le potentiel de la microscopie ptychographique de Fourier dans l’analyse des frottis sanguins minces. Plusieurs r\u00e9sultats ont \u00e9t\u00e9 obtenus gr\u00e2ce \u00e0 une approche multidisciplinaire int\u00e9grant l’apprentissage en profondeur et la microscopie. Nous nous concentrons d’abord sur le probl\u00e8me limit\u00e9 de la d\u00e9tection des parasites pour le diagnostic du paludisme. L’exploitation conjointe de l’intensit\u00e9 et de la phase permet d’am\u00e9liorer les performances d’un d\u00e9tecteur de r\u00e9seau neuronal profond. \u00c0 cette fin, un CNN \u00e0 valeurs complexes a \u00e9t\u00e9 introduit dans l’architecture Faster-RCNN pour une extraction efficace des caract\u00e9ristiques. Ensuite, nous examinons une application plus complexe, \u00e0 savoir la classification des globules blancs, o\u00f9 les avantages de l’exploitation conjointe de l’intensit\u00e9 et de la phase ont \u00e9galement \u00e9t\u00e9 confirm\u00e9s. Nous nous int\u00e9ressons \u00e9galement au probl\u00e8me du d\u00e9s\u00e9quilibre des classes rencontr\u00e9 dans cette t\u00e2che, nous proposons un nouveau mod\u00e8le GAN inform\u00e9 par la physique d\u00e9di\u00e9 \u00e0 la g\u00e9n\u00e9ration d’images d’intensit\u00e9 et de phase. Ce mod\u00e8le \u00e9vite le probl\u00e8me de mode collapse rencontr\u00e9 avec l’impl\u00e9mentation habituelle des GAN. Enfin, nous consid\u00e9rons l’optimisation de la conception du microscope FPM. \u00c0 cette fin, nous explorons des strat\u00e9gies combinant simulations, r\u00e9seaux neuronaux et mod\u00e9lisation de la formation d’images. Nous d\u00e9montrons que la FPM peut utiliser des r\u00e9solutions faibles sans compromettre significativement les performances. Cette th\u00e8se souligne l’int\u00e9r\u00eat d’adapter l’apprentissage automatique en lien avec les principes de la microscopie et met en \u00e9vidence le potentiel de la microscopie ptychographique de Fourier pour les futurs syst\u00e8mes de diagnostic automatis\u00e9s.
<\/p>\n\n\n\n
Abstract :<\/strong><\/p>\n\n\n\nDigital pathology presents today a fundamental tool for medical diagnosis, exploiting technological advances in digitalization to transform biological samples into digital data, thus facilitating their visualization and analysis. However, these methods, often based on conventional microscopy, encounter limitations that sometimes hinder their effectiveness. From this perspective, unconventional imaging methods such as Fourier ptychographic microscopy offer promising prospects for overcoming these limitations. Indeed, FPM offers access to the phase in complement of the intensity and allows examining a large Field of View at a high resolution at a reasonable design cost. This thesis explores Fourier ptychographic microscopy (FPM) \u2019s potential in thin blood smear analysis. Several results have been obtained thanks to a multidisciplinary approach integrating deep learning and microscopy. We have first focused our attention on the problem of limited complexity of parasite detection for malaria diagnosis. The joint exploitation of intensity and phase is shown to improve the performance of a deep network detector. To this end, a complex-valued CNN has been introduced in Faster-RCNN architecture for efficient feature extraction. Secondly, we have considered a more complex application, namely the classification of white blood cells, where the benefits of joint exploitation of intensity and phase were also confirmed. Furthermore, to reduce the imbalance of classes encountered in this task, we propose a novel physics-informed GAN model dedicated to generating intensity and phase images. This model avoids the mode collapse problem faced with usual GAN implementation. Finally, we have considered optimizing the FPM microscope design. To this end, we explore strategies combining simulations, neural networks, and image formation modeling. We demonstrate that FPM can use low resolutions without significantly compromising performance. This thesis underscores the interest in tailoring machine learning in connection to microscopy principles and highlights the potential of Fourier ptychographic microscopy for future automated diagnosis systems.<\/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 Madame Houda HASSINI Autoris\u00e9e \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en : 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