7 research outputs found

    Détection du cancer dans les images de tomographie par cohérence optique plein champ

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    Cancer is a leading cause of death worldwide making it a major public health concern. Different biomedical imaging techniques accompany both research and clinical efforts towards improving patient outcome. In this work we explore the use of a new family of imaging techniques, static and dynamic full field optical coherence tomography, which allow for a faster tissue analysis than gold standard histology. In order to facilitate the interpretation of this new imaging, we develop several exploratory methods based on data curated from clinical studies. We propose an analytical method for a better characterization of the raw dynamic interferometric signal, as well as multiple diagnostic support methods for the images. Accordingly, convolutional neural networks were exploited under various paradigms: (i) fully supervised learning, whose prediction capability surpasses the pathologist performance; (ii) multiple instance learning, which accommodates the lack of expert annotations; (iii) contrastive learning, which exploits the multi-modality of the data. Moreover, we highly focus on method validation and decoding the trained "black box" models to ensure their good generalization and to ultimately find specific biomarkers.Le cancer est une des principales cause de d√©c√®s dans le monde et donc un probl√®me majeur de sant√© publique. Plusieurs techniques d'imagerie biom√©dicale servent √† la recherche et aux efforts cliniques pour am√©liorer le pronostic du patient. Nous √©tudions l'utilisation d'une nouvelle famille de techniques d'imagerie, la tomographie par coh√©rence optique plein champ statique et dynamique, qui permet une analyse du tissu plus rapide que la technique de r√©f√©rence en histopathologie. Afin de faciliter l'interpr√©tation de cette nouvelle imagerie, nous d√©veloppons plusieurs m√©thodes exploratoires bas√©es sur des donn√©es issues d'√©tudes cliniques. Nous proposons une m√©thode analytique pour une meilleure caract√©risation du signal interf√©rom√©trique dynamique brut, ainsi que de multiples m√©thodes d'aide au diagnostic √† partir des images. Pour cela, des r√©seaux neuronaux convolutifs ont √©t√© exploit√©s sous diff√©rents paradigmes: (i) apprentissage enti√®rement supervis√©, dont la capacit√© de pr√©diction d√©passe la performance du pathologiste; (ii) apprentissage par instances multiples, qui permet de surmonter le manque d‚Äôannotations d‚Äôexperts; (iii) apprentissage contrastif, qui exploite la multi-modalit√© des donn√©es. Nous portons une grande attention √† la validation et au d√©cryptage des mod√®les bo√ģte noire pour garantir leur bonne g√©n√©ralisation et enfin trouver des biomarqueurs sp√©cifiques

    Détection du cancer dans les images de tomographie par cohérence optique plein champ

    No full text
    Cancer is a leading cause of death worldwide making it a major public health concern. Different biomedical imaging techniques accompany both research and clinical efforts towards improving patient outcome. In this work we explore the use of a new family of imaging techniques, static and dynamic full field optical coherence tomography, which allow for a faster tissue analysis than gold standard histology. In order to facilitate the interpretation of this new imaging, we develop several exploratory methods based on data curated from clinical studies. We propose an analytical method for a better characterization of the raw dynamic interferometric signal, as well as multiple diagnostic support methods for the images. Accordingly, convolutional neural networks were exploited under various paradigms: (i) fully supervised learning, whose prediction capability surpasses the pathologist performance; (ii) multiple instance learning, which accommodates the lack of expert annotations; (iii) contrastive learning, which exploits the multi-modality of the data. Moreover, we highly focus on method validation and decoding the trained "black box" models to ensure their good generalization and to ultimately find specific biomarkers.Le cancer est une des principales cause de d√©c√®s dans le monde et donc un probl√®me majeur de sant√© publique. Plusieurs techniques d'imagerie biom√©dicale servent √† la recherche et aux efforts cliniques pour am√©liorer le pronostic du patient. Nous √©tudions l'utilisation d'une nouvelle famille de techniques d'imagerie, la tomographie par coh√©rence optique plein champ statique et dynamique, qui permet une analyse du tissu plus rapide que la technique de r√©f√©rence en histopathologie. Afin de faciliter l'interpr√©tation de cette nouvelle imagerie, nous d√©veloppons plusieurs m√©thodes exploratoires bas√©es sur des donn√©es issues d'√©tudes cliniques. Nous proposons une m√©thode analytique pour une meilleure caract√©risation du signal interf√©rom√©trique dynamique brut, ainsi que de multiples m√©thodes d'aide au diagnostic √† partir des images. Pour cela, des r√©seaux neuronaux convolutifs ont √©t√© exploit√©s sous diff√©rents paradigmes: (i) apprentissage enti√®rement supervis√©, dont la capacit√© de pr√©diction d√©passe la performance du pathologiste; (ii) apprentissage par instances multiples, qui permet de surmonter le manque d‚Äôannotations d‚Äôexperts; (iii) apprentissage contrastif, qui exploite la multi-modalit√© des donn√©es. Nous portons une grande attention √† la validation et au d√©cryptage des mod√®les bo√ģte noire pour garantir leur bonne g√©n√©ralisation et enfin trouver des biomarqueurs sp√©cifiques

    Détection du cancer dans les images de tomographie par cohérence optique plein champ

    No full text
    Le cancer est une des principales cause de d√©c√®s dans le monde et donc un probl√®me majeur de sant√© publique. Plusieurs techniques d'imagerie biom√©dicale servent √† la recherche et aux efforts cliniques pour am√©liorer le pronostic du patient. Nous √©tudions l'utilisation d'une nouvelle famille de techniques d'imagerie, la tomographie par coh√©rence optique plein champ statique et dynamique, qui permet une analyse du tissu plus rapide que la technique de r√©f√©rence en histopathologie. Afin de faciliter l'interpr√©tation de cette nouvelle imagerie, nous d√©veloppons plusieurs m√©thodes exploratoires bas√©es sur des donn√©es issues d'√©tudes cliniques. Nous proposons une m√©thode analytique pour une meilleure caract√©risation du signal interf√©rom√©trique dynamique brut, ainsi que de multiples m√©thodes d'aide au diagnostic √† partir des images. Pour cela, des r√©seaux neuronaux convolutifs ont √©t√© exploit√©s sous diff√©rents paradigmes: (i) apprentissage enti√®rement supervis√©, dont la capacit√© de pr√©diction d√©passe la performance du pathologiste; (ii) apprentissage par instances multiples, qui permet de surmonter le manque d‚Äôannotations d‚Äôexperts; (iii) apprentissage contrastif, qui exploite la multi-modalit√© des donn√©es. Nous portons une grande attention √† la validation et au d√©cryptage des mod√®les bo√ģte noire pour garantir leur bonne g√©n√©ralisation et enfin trouver des biomarqueurs sp√©cifiques.Cancer is a leading cause of death worldwide making it a major public health concern. Different biomedical imaging techniques accompany both research and clinical efforts towards improving patient outcome. In this work we explore the use of a new family of imaging techniques, static and dynamic full field optical coherence tomography, which allow for a faster tissue analysis than gold standard histology. In order to facilitate the interpretation of this new imaging, we develop several exploratory methods based on data curated from clinical studies. We propose an analytical method for a better characterization of the raw dynamic interferometric signal, as well as multiple diagnostic support methods for the images. Accordingly, convolutional neural networks were exploited under various paradigms: (i) fully supervised learning, whose prediction capability surpasses the pathologist performance; (ii) multiple instance learning, which accommodates the lack of expert annotations; (iii) contrastive learning, which exploits the multi-modality of the data. Moreover, we highly focus on method validation and decoding the trained "black box" models to ensure their good generalization and to ultimately find specific biomarkers

    Image compressed sensing recovery using intra-block prediction

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    International audienc

    Adaptive saliency-based compressive sensing image reconstruction

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    International audienceThis paper proposes an adaptive compressive sensing reconstruction method which provides a higher recovered image quality. Based on an initial compressive sampling reconstruction at a given sampling rate, the visually salient regions of the image that are more conspicuous to the human visual system are extracted using a classical graph-based method. The target acquisition subrate is further adaptively allocated among these regions, such that the new acquisition will favor the interest areas. The measurements produced by this adaptive method are fully compatible with the existing sparse reconstruction algorithms, which favors the utilization of the proposed scheme. Simulation results show that the saliency-based compressive sensing recovery method outperforms the conventional sparse reconstruction algorithms in terms of image quality at the same target sampling ratio with a smaller increment in the complexity

    The Lifecycle of a Neural Network in the Wild: A Multiple Instance Learning Study on Cancer Detection from Breast Biopsies Imaged with Novel Technique

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    International audienceIn the context of tissue examination for breast cancer assessment, we propose a label-free imaging based on Optical Coherence Tomography (OCT) signal combined with a multiple instance learning (MIL) model to respond to a critical need for fast at point-of-care diagnosis: biopsy or surgery time. This new imaging, Dynamic Cell Imaging (DCI), is the time-resolved variant of Full-Field OCT (FFOCT) and offers an intra-cellular resolution of about 1 micron, together with optical sectioning and an improved cell contrast. In order to tackle the challenges of limited data and annotations, while remaining in the scope of interpretability, we design an instance-level MIL model with a focus on adapted data sampling. The interest of this method is that it incorporates taskspecific feature learning and also produces instance predictions. For a dataset of 150 core-needle biopsies, we achieve a considerable improvement of more than 20 percentage points in specificity and about 10 in accuracy by leveraging intradomain (as compared to extra-domain) pre-training

    Automatic diagnosis and biopsy classification with dynamic Full-Field OCT and machine learning

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    Abstract The adoption of emerging imaging technologies in the medical community is often hampered if they provide a new unfamiliar contrast that requires experience to be interpreted. Here, in order to facilitate such integration, we developed two complementary machine learning approaches, respectively based on feature engineering and on convolutional neural networks (CNN), to perform automatic diagnosis of breast biopsies using dynamic full field optical coherence tomography (D-FF-OCT) microscopy. This new technique provides fast, high resolution images of biopsies with a contrast similar to H&E histology, but without any tissue preparation and alteration. We conducted a pilot study on 51 breast biopsies, and more than 1,000 individual images, and performed standard histology to obtain each biopsy diagnosis. Using our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level, and above 96% at the biopsy level. Finally, we proposed different strategies to narrow down the spatial scale of the automatic segmentation in order to be able to draw the tumor margins by drawing attention maps with the CNN approach, or by performing high resolution precise annotation of the datasets. Altogether, these results demonstrate the high potential of D-FF-OCT coupled to machine learning to provide a rapid, automatic, and accurate histopathology diagnosis
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