Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 12 of 12 for “"Whole Slide Images"”.
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Deep learning on whole-slide images for early detection and risk prediction of oesophageal cancer
… detected quantity of BE in a pathology slide and the length of the BE segment identified from endoscopy. Beyond oesophageal cancer, the inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis, and monitoring of disease. However, WSIs present a …
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Histopathological image analysis with connections to genomics
… features from hematoxylin and eosin stained images that have meaningful connections to genomic data. Additionally, with the advent of whole slide images, significantly more data representing the variation in nuclear characteristics and tumor heterogeneity is available, which can aid in …
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Investigation of KimiaNet's and DenseNet's deep features in lung cancer subtypes
… as a simple yet efficient representation of whole slide images (WSI). Through exploring deep features of lung cancer, it was discovered that some of these deep features have strong correlations with either lung adenocarcinoma (LUAD) or lung squamous carcinoma (LUSC). A deep feature-specific …
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Data-Driven General Purpose Foundation Models for Computational Pathology
… general-purpose encoder models for pathology images: one using paired image-text data, and another leveraging self-supervised learning on large-scale unlabeled images. Additionally, I will examine downstream applications of these foundation models, including zero-shot transfer to gigapixel …
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DEEP LEARNING ALGORITHM FOR MOLECULAR CLASSIFICATION OF ENDOMETRIAL CANCER FROM WHOLE SLIDE HISTOPATHOLOGY IMAGES
… EC molecular subtypes directly from H&E-stained whole-slide images (WSIs). From an initial cohort of 1,362 cases, 230 FFPE WSIs were selected and annotated to train three sequential binary classifiers (POLEmut vs non-POLE, MMRd vs non-MMRd, p53-abn vs NSMP), forming a hierarchical, clinically …
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Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
… tasks involving only small to medium sized-images, neither of which are applicable to the emerging field of computational pathology where there are limited publicly available paired image-text datasets and each image can span up to 100,000 x 100,000 pixels. In this paper we present MI-Zero, …
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Transforming Medical Image Segmentation with Enhanced U-Net Architectures and Adaptive Transfer Learning
… OCU-Net for oral cancer tissue segmentation from whole slide images (WSI) stained with Hematoxylin and Eosin (H&E). Furthermore, we introduce the "U-Framework", a comprehensive guide in designing and optimizing U-Net models. This framework encompasses key decisions related to architecture, …
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A Deep Learning Approach to Automated Coeliac Disease Diagnosis
… biopsies can now be scanned digitally as whole-slide images (WSI), allowing biopsy analysis to be automated using digital image processing. In this thesis, I address the following question: “Can modern machine learning techniques provide a sensitive, objective and reproducible test for CD …
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Segregation similarity loss in morphological ranking of image search in histopathology
… of many cancers types, and the large size of Whole Slide Images (WSIs), the analysis of histopathology images is challenging. To come up with this challenge, AI algorithms, such as deep learning (DL) are used to automate image analysis efficiently and accurately. In this study, some DL methods …
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An Assessment of Universal Tumour Associated Antigens in Primary Liver Neoplasms
… quantified using digital pathology techniques on whole-slide images. Haematological protein levels were assessed using the enzyme linked immunosorbent assay. All of these characteristics were then compared with clinical measures such as tumour size, grade, stage, vascular invasion, overall …
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Reframing Cox Proportional Hazards Model for Big Data and Neural Networks
… ultra-high dimensional features, or images. We propose frameworks that are computationally efficient and stable and are amenable to stochastic-based optimization algorithms. Our proposed frameworks scale up to extremely large datasets that do not fit into memory. The aim of survival …
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Texture-based Deep Neural Network for Histopathology Cancer Whole Slide Image (WSI) Classification
<p>Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques. However, manual examination and diagnosis with WSIs is time-consuming and tiresome. Recently, deep convolutional neural …