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 14 of 14 for “"Histopathology images"”.
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Tumour Localisation in Histopathology Images
… in a rotation invariant manner, suitable for histopathology images. To incorporate essential contextual information, methods which utilise posterior tumour probabilities in an iterative manner are proposed. Results showed pixel-level agreements between automated and manual tumour segmentation …
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Deep learning for processing histopathology images
Histopathology is the study and diagnosis of disease via tissue microscopy and it is currently the ‘gold-standard‘ in formally diagnosing many types of disease including cancers.<br/>Due to increasing workloads on pathologists, there is a growing need for automated image analysis pipelines that are …
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Learning from histopathology images: AI-driven biomarkers for pancreatic ductal adenocarcinoma
Contains fulltext : 332320.pdf (Publisher’s version ) (Closed access)
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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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DEEP LEARNING ALGORITHM FOR MOLECULAR CLASSIFICATION OF ENDOMETRIAL CANCER FROM WHOLE SLIDE HISTOPATHOLOGY IMAGES
… 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 aligned …
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Registration of pre-operative lung cancer PET/CT scans with post-operative histopathology images
… they are compared against the gold standard of histopathology.The aim of this retrospective study was to build a robust imaging framework for registering in vivo and post-operative scans from lung cancer patients, in order to have a global, pathology-validated multimodality map of the tumour and …
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Deep Learning in Oesophageal Cancer Development: Integrating Multi-Stain Histopathology Images of the Capsule Sponge
… I trained seg- mentation models for H&E and IHC images, followed by classification models for p53 (positive, equivocal, negative), TFF3 (positive, negative), and H&E glands (normal, atypical, dysplastic). Trained on enriched subsets of the DELTA and BEST2 trials and validated on independent …
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Segregation similarity loss in morphological ranking of image search in histopathology
Histopathology is the study of changes in tissue caused by diseases such as cancer. It plays an important role to diagnose the cancers. Regarding the large variation of many cancers types, and the large size of Whole Slide Images (WSIs), the analysis of histopathology images is challenging. To come …
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Deep Learning-Based Comprehensive Pathology Image Analysis
… have provided great tools for the analysis of histopathology images. Deep learning algorithms can aid in the routine diagnostics and have the potential to extract hidden information directly from slide images, providing valuable information for healthcare professionals. This dissertation …
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One-Shot Learning Model for Cancer Diagnosis from Histopathological Images
… Networks (DNN) have been proposed for analyzing histopathology images for various cancer types and datasets. Typical challenges for a deep neural network to operate in this setting are limited datasets, gigapixel images and small percentage and high variability of nuclei indicative of malignant …
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Interpretable Tumor Localization in Bladder Cancer Histopathology Using Deep Multiple Instance Learning
Deep learning has emerged in cancer histopathology as a tool for predicting clinical and molecular properties of a patient’s disease, thereby connecting slide with function. This concept is especially relevant to bladder cancer, where molecular and histopathologic heterogeneity is known to impact …
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Multimodal data analysis applied to a medical setting
… been studied using genetic data, or images alone. To understand the biology of such diseases, joint analysis of multiple data modalities could provide interesting insights. We propose the use of canonical correlation analysis (CCA) as a preliminary discovery tool for identifying …
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Breast cancer diagnosis using Fourier transform infrared imaging and statistical learning
… involves manual interpretation of stained images for patient diagnosis. This is prone to inter- observer variability leading to low concordance rates amongst pathologists. Further, since structural features are mostly just defined for epithelial alterations during tumor progression, the use …
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Using Tissue Morphology to Infer Intra-Tumor Variation in Molecular State and Response
… prohibitive for broad adoption. In contrast, histopathology slides are ubiquitous and, as evidenced by their extensive use in clinical diagnosis, capture key aspects of tumor biology. However, the scale and complexity of the morphological phenotypes in tissue slides render manual …