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 9 of 9 for “"Computational pathology"”.
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Data-Driven General Purpose Foundation Models for Computational Pathology
The field of computational pathology has undergone a remarkable transformation in recent years. Researchers have leveraged supervised and weakly-supervised deep learning with varying degrees of success to address a wide range of tasks, including cancer subtyping and grading, metastasis detection, …
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Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer with Gene Expression and Computational Pathology
… to treatment based on gene expression and computational pathology. Firstly, I attempted to develop a clinically practical approach for classifying breast tumours using RNA from routine formalin fixed paraffin embedded (FFPE) histopathological samples. Breast cancers can be classified into …
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Advancing Veterinary Cytology with Deep Learning: Development, Validation, and Best Practices
… have led to rapid growth in both digital and computational pathology. With this growth, the field of veterinary medicine has seen a significant expansion of diagnostics in computational pathology, specifically in artificial intelligence. Yet, there is little information on test validation, …
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Histopathological image analysis with connections to genomics
… possible. This work demonstrates the efficacy of computational histopathological image analysis to extract meaningful quantitative nuclear and cellular features from hematoxylin and eosin stained images that have meaningful connections to genomic data. Additionally, with the advent of whole slide …
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Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
… 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, a simple and intuitive framework for unleashing the zero-shot …
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Deep learning on whole-slide images for early detection and risk prediction of oesophageal cancer
This dissertation introduces novel computational techniques to identify patients at particularly high risk for progressing from Barrett’s oesophagus (BE) to oesophageal adenocarcinoma (EAC) earlier and more accurately using data from a minimally-invasive cell collection device. It also introduces a …
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Reframing Cox Proportional Hazards Model for Big Data and Neural Networks
… 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 analysis is to assess the connection …
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Spatial Transcriptomics Analysis Reveals Transcriptomic and Cellular Topology Associations in Breast and Prostate Cancers
Background: Cancer is the leading cause of death worldwide and as a result is one of the most studied topics in public health. Breast cancer and prostate cancer are the most common cancers among women and men respectively. Gene expression and image features are independently prognostic of patient …