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Artykuły w czasopismach na temat "Classification de lames entières"
Ben Abdelkrim, Soumaya, Soumaya Rammeh, Amel Trabelsi, Lilia Ben Yacoub-Abid, Nabil Ben Sorba, Lilia Jaïdane i Moncef Mokni. "Reproductibilité des classifications OMS 1973 et OMS 2004 des tumeurs urothéliales papillaires de la vessie". Canadian Urological Association Journal 6, nr 6 (13.12.2012): 230. http://dx.doi.org/10.5489/cuaj.121.
Pełny tekst źródłaBugeaud, Virginie. "Classification analytique et théorie de Galois locales des modules aux q-différences à pentes non entières". Comptes Rendus Mathematique 349, nr 19-20 (listopad 2011): 1037–39. http://dx.doi.org/10.1016/j.crma.2011.09.002.
Pełny tekst źródłaD., Collectif Marco, Marcel Bénabou, Baptiste Brun, Jean-Luc Deschamps, Dominique de Liège, Yan Pélissier i Olivier Vidal. "Marco Decorpeliada, l'homme aux schizomètres". Déméter, nr 5 | Été (1.09.2020). http://dx.doi.org/10.54563/demeter.114.
Pełny tekst źródłaBELINGA ATEBA, J., Gilles Yvans AKAMBA i DZANA Jean-Guy. "ÉVALUATION QUANTITATIVE DE LA SENSIBILITÉ HYDROGÉOMORPHOLOGIQUE DU LIT À MÉANDRES DE LA MENOUA À SANTCHOU (OUEST-CAMEROUN)". International Journal for Advanced Studies and Research in Africa 13, nr 1 (30.06.2023). http://dx.doi.org/10.58808/ijr1311.
Pełny tekst źródłaRozprawy doktorskie na temat "Classification de lames entières"
Lerousseau, Marvin. "Weakly Supervised Segmentation and Context-Aware Classification in Computational Pathology". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG015.
Pełny tekst źródłaAnatomic pathology is the medical discipline responsible for the diagnosis and characterization of diseases through the macroscopic, microscopic, molecular and immunologic inspection of tissues. Modern technologies have made possible the digitization of tissue glass slides into whole slide images, which can themselves be processed by artificial intelligence to enhance the capabilities of pathologists. This thesis presented several novel and powerful approaches that tackle pan-cancer segmentation and classification of whole slide images. Learning segmentation models for whole slide images is challenged by an annotation bottleneck which arises from (i) a shortage of pathologists, (ii) an intense cumbersomeness and boring annotation process, and (iii) major inter-annotators discrepancy. My first line of work tackled pan-cancer tumor segmentation by designing two novel state-of-the-art weakly supervised approaches that exploit slide-level annotations that are fast and easy to obtain. In particular, my second segmentation contribution was a generic and highly powerful algorithm that leverages percentage annotations on a slide basis, without needing any pixelbased annotation. Extensive large-scale experiments showed the superiority of my approaches over weakly supervised and supervised methods for pan-cancer tumor segmentation on a dataset of more than 15,000 unfiltered and extremely challenging whole slide images from snap-frozen tissues. My results indicated the robustness of my approaches to noise and systemic biases in annotations. Digital slides are difficult to classify due to their colossal sizes, which range from millions of pixels to billions of pixels, often weighing more than 500 megabytes. The straightforward use of traditional computer vision is therefore not possible, prompting the use of multiple instance learning, a machine learning paradigm consisting in assimilating a whole slide image as a set of patches uniformly sampled from it. Up to my works, the greater majority of multiple instance learning approaches considered patches as independently and identically sampled, i.e. discarded the spatial relationship of patches extracted from a whole slide image. Some approaches exploited such spatial interconnection by leveraging graph-based models, although the true domain of whole slide images is specifically the image domain which is more suited with convolutional neural networks. I designed a highly powerful and modular multiple instance learning framework that leverages the spatial relationship of patches extracted from a whole slide image by building a sparse map from the patches embeddings, which is then further processed into a whole slide image embedding by a sparse-input convolutional neural network, before being classified by a generic classifier model. My framework essentially bridges the gap between multiple instance learning, and fully convolutional classification. I performed extensive experiments on three whole slide image classification tasks, including the golden task of cancer pathologist of subtyping tumors, on a dataset of more than 20,000 whole slide images from public data. Results highlighted the superiority of my approach over all other widespread multiple instance learning methods. Furthermore, while my experiments only investigated my approach with sparse-input convolutional neural networks with two convolutional layers, the results showed that my framework works better as the number of parameters increases, suggesting that more sophisticated convolutional neural networks can easily obtain superior results
Pirovano, Antoine. "Computer-aided diagnosis methods for cervical cancer screening on liquid-based Pap smears using convolutional neural networks : design, optimization and interpretability". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT011.
Pełny tekst źródłaCervical cancer is the second most important cancer for women after breast cancer. In 2012, the number of cases exceeded 500,000 worldwide, among which half turned to be deadly.Until today, primary cervical cancer screening is performed by a regular visual analysis of cells, sampled by pap-smear by cytopathologists under brightfield microscopy in pathology laboratories. In France, about 5 millions of cervical screening are performed each year and about 90% lead to a negative diagnosis (i.e. no pre-cancerous changes detected). Yet, these analyses under microscope are extremely tedious and time-consuming for cytotechnicians and can require the joint opinion of several experts. This process has an impact on the capacity to tackle this huge amount of cases and to avoid false negatives that are the main cause of treatment delay. The lack of automation and traceability of screening is thus becoming more critical as the number of cyto-pathologists decreases. In that respect, the integration of digital tools in pathology laboratories is becoming a real public health stake for patients and the privileged path for the improvement of these laboratories. Since 2012, deep learning methods have revolutionized the computer vision field, in particular thanks to convolutional neural networks that have been applied successfully to a wide range of applications among which biomedical imaging. Along with it, the whole slide imaging digitization process has opened the opportunity for new efficient computer-aided diagnosis methods and tools. In this thesis, after motivating the medical needs and introducing the state-of-the-art deep learning methods for image processing and understanding, we present our contribution to the field of computer vision tackling cervical cancer screening in the context of liquid-based cytology. Our first contribution consists in proposing a simple regularization constraint for classification model training in the context of ordinal regression tasks (i.e. ordered classes). We prove the advantage of our method on cervical cells classification using Herlev dataset. Furthermore, we propose to rely on explanations from gradient-based explanations to perform weakly-supervised localization and detection of abnormality. Finally, we show how we integrate these methods as a computer-aided tool that could be used to reduce the workload of cytopathologists.The second contribution focuses on whole slide classification and the interpretability of these pipelines. We present in detail the most popular approaches for whole slide classification relying on multiple instance learning, and improve the interpretability in a context of weakly-supervised learning through tile-level feature visualizations and a novel manner of computing explanations of heat-maps. Finally, we apply these methods for cervical cancer screening by using a weakly trained “abnormality” detector for region of interest sampling that guides the training
Naylor, Peter. "Du phénotypage cellulaire à la classification de lames digitales : Une application au traitement du cancer du sein triple-négatif". Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEM051.
Pełny tekst źródłaThe rise of digital pathology and with it the challenges of histopathology analysis have been the focus of a worldwide effort in the overall fight against cancer. In parallel, the recent success of automated decision-making, machine learning, and specifically deep learning, have revolutionised the basis of research as we know today. In this thesis, we tackle the prediction of treatment response in triple-negative breast cancer patients with two different approaches that reach similar outcomes. The first line of approach, based on the recent success of computer vision, extracts learned features from the data in order to perform classification. The second line of approach forces the information flow to pass through nuclei segmentation. In particular, it allows the incorporation of high-resolution information on to a lower resolution overview. Yet while this approach is more appealing as it is based on the analysis and quantification of a precise biological element, nuclei segmentation is troublesome. While solving the task of nuclei segmentation with deep learning, we propose a new formulation for nuclei segmentation which excels at separating touching objects