Sökning: "digital histopathology"

Visar resultat 1 - 5 av 17 avhandlingar innehållade orden digital histopathology.

  1. 1. Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis

    Författare :Andreas Kårsnäs; Robin Strand; Carolina Wählby; Ewert Bengtsson; Anant Madabhushi; Uppsala universitet; []
    Nyckelord :ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; image analysis; breast cancer diagnosis; digital histopathology; immunohistochemistry; biomarker quantification; Computerized Image Processing; Datoriserad bildbehandling;

    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. 2. Synthetic data for visual machine learning : A data-centric approach

    Författare :Apostolia Tsirikoglou; Jonas Unger; Gabriel Eilertsen; Anders Ynnerman; Philipp Slusallek; Linköpings universitet; []
    Nyckelord :ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Training data; Synthetic images; Computer graphics; Generative modeling; Natural images; Histopathology; Digital pathology; Machine learning; Deep learning;

    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. 3. Spectral Image Processing with Applications in Biotechnology and Pathology

    Författare :Milan Gavrilovic; Carolina Wählby; Ewert Bengtsson; Ingrid Carlbom; Robert Murphy; Uppsala universitet; []
    Nyckelord :ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; color theory; light microscopy; spectral imaging; image analysis; digital image processing; mathematical modeling; estimation; noise models; spectral decomposition; color decomposition; colocalization; cross-talk; autofluorescence; tissue separation; prostate cancer; biomedical applications; molecular biotechnology; histopathology; Computerized Image Processing; Datoriserad bildbehandling;

    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. 4. Automated Tissue Image Analysis Using Pattern Recognition

    Författare :Jimmy Azar; Anders Hast; Ewert Bengtsson; Martin Simonsson; Marco Loog; Uppsala universitet; []
    Nyckelord :ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; tissue image analysis; pattern recognition; digital histopathology; immunohistochemistry; paired antibodies; histological stain evaluation; Computerized Image Processing; Datoriserad bildbehandling;

    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. 5. Deep Learning for Digital Pathology in Limited Data Scenarios

    Författare :Karin Stacke; Jonas Unger; Gabriel Eilertsen; Claes Lundström; Henning Müller; Linköpings universitet; []
    Nyckelord :MEDICAL AND HEALTH SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; Medical imaging; Digital pathology; Radiology; Machine learning; Deep learning.;

    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