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Introduction and Motivation - Coggle Diagram
Introduction and Motivation
Fibrosis in disease
Fibrosis happens in diseases such as cancer
Fibrosis is associated with disease development
Characterization of fibrosis is gaining attentions, progresses in disease diagnosis
Assessment of fibrosis in histopathology
Imaging methods for visualizing fibrosis, challenges for clinical use
Computational methods for quantifying collagen fibers from images. Drawback and limitations.
Associating image biomarkers with gene expression: currently only use empirical observations
Deep learning in histopathological image analysis
Deep learning methods used for classification, disease detection, segmentation, in histopathology
Develop deep learning-based tools for visualizing collagen fibers in histopathology
Develop deep learning-based methods for whole slide image analysis
Challenges in histopathological image analysis and whole slide image analysis: availability of scanner, weak labels, interoperability (safety concern)
Three aims:
Develop deep learning-based tools for enhanced visualization in clinical histopathology
Visualization of collagen fiber
Visualization of whole slide
Develop deep learning models for whole slide image analysis
Disease detection: case - PDAC and Pancreatitis
Practical safety concern: confidence and uncertainty
Generative model for training fiber tracking algorithms
Associating whole slide image with collagen expression genetic indicators, reveal gene-associated collagen patterns
Computational instrumentation for histopathology and microscopy (mostly collaboration)
Extendable open-source software for laser scanning microscopy and run-time image analysis (OpenScan and Micro-Manager)
Visualization and assessment of fibrotic tissue microenvironment in 3D histopathology