Sökning: "digital histopathology"
Visar resultat 1 - 5 av 19 avhandlingar innehållade orden digital histopathology.
1. Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis
Sammanfattning : In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. LÄS MER
2. Synthetic data for visual machine learning : A data-centric approach
Sammanfattning : Deep learning allows computers to learn from observations, or else training data. Successful application development requires skills in neural network design, adequate computational resources, and a training data distribution that covers the application do-main. LÄS MER
3. Spectral Image Processing with Applications in Biotechnology and Pathology
Sammanfattning : Color theory was first formalized in the seventeenth century by Isaac Newton just a couple of decades after the first microscope was built. But it was not until the twentieth century that technological advances led to the integration of color theory, optical spectroscopy and light microscopy through spectral image processing. LÄS MER
4. Automated Tissue Image Analysis Using Pattern Recognition
Sammanfattning : Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy. LÄS MER
5. Deep Learning for Digital Pathology in Limited Data Scenarios
Sammanfattning : The impressive technical advances seen for machine learning algorithms in combination with the digitalization of medical images in the radiology and pathology departments show great promise in introducing powerful image analysis tools for image diagnostics. In particular, deep learning, a subfield within machine learning, has shown great success, advancing fields such as image classification and detection. LÄS MER