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 8 of 8 for “"Cancer Subtyping"”.
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Specialized Named Entity Recognition for Breast Cancer Subtyping
… example of this is the field of breast cancer biology where over 2 million people are diagnosed worldwide every year and billions of dollars are spent on research. Breast cancer biology literature and research relies on a highly specific domain with unique language and vocabulary, and …
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Identifying and Minimizing Underspecification in Breast Cancer Subtyping
… and minimize underspecification of deep learning cancer subtype predictors. To address these goals, this work details the development of Predicting Underspecification Monitoring Pipeline (PUMP), a software tool to provide methodology for data analysis, stress testing, and model evaluation. In this …
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CancerSubtyper: A Web-Based Deep Learning Platform for Cancer Subtyping Through DNA Methylation Data
Cancer subtyping plays a critical role in understanding tumor heterogeneity, predicting patient outcomes, and guiding personalized therapies. While DNA methylation data offers an informative molecular source for subtyping, leveraging its signals across cohorts remains challenging due to high …
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Data-Driven General Purpose Foundation Models for Computational Pathology
… to address a wide range of tasks, including cancer subtyping and grading, metastasis detection, survival and treatment response prediction, tumor site-of-origin identification, mutation prediction, biomarker screening, and more. However, traditional task-specific models often require …
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Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
… of 70.2% across three different real-world cancer subtyping tasks.
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Distributional and relational inductive biases for graph representation learning in biomedicine
… automatically construct sparse neural models for cancer subtyping. Finally, we present a state-of-the-art cell deconvolution model for spatial transcriptomics data using the positional relationships between observations in the dataset.
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Detection and monitoring of treatment resistance in metastatic cancer through non-invasive DNA methylation-based biomarkers
… biological, and clinical heterogeneity of breast cancer (BC), the main tumor type investigated throughout this research, is examined, along with the complexities of BC subtype classification and contingent therapeutic strategies. Particular attention is given to the emergence of resistance …