Sökning: "Gabriel Eilertsen"

Hittade 5 avhandlingar innehållade orden Gabriel Eilertsen.

1. 1. The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

   Författare :Gabriel Eilertsen; Jonas Unger; Rafał Mantiuk.; Anders Ynnerman; Erik Reinhard; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; high dynamic range imaging; tone-mapping; video tone-mapping; HDR video encoding; HDR image reconstruction; inverse tone-mapping; machine learning; deep learning;

   Sammanfattning : Techniques for high dynamic range (HDR) imaging make it possible to capture and store an increased range of luminances and colors as compared to what can be achieved with a conventional camera. This high amount of image information can be used in a wide range of applications, such as HDR displays, image-based lighting, tone-mapping, computer vision, and post-processing operations. LÄS MER

3. 2. Inverse Problems for Tumour Growth Models and Neural ODEs

   Författare :Rym Jaroudi; George Baravdish; Tomas Johansson; Jonas Unger; Gabriel Eilertsen; Lukáš Malý; Torbjörn Lundh; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES;

   Sammanfattning : This thesis concerns the application of methods and techniques from the theory of inverse problems and differential equations to study models arising in the areas of mathematical oncology and deep learning. The first problem studied is to develop methods to perform numerical simulations with full 3-dimensional brain imaging data of reaction-diffusion models for tumour growth forwards as well as backwards in time with the goal of enabling the numerical reconstruction of the source of the tumour given an image (or similar data) at a later stage in time of the tumour. LÄS MER

4. 3. Generalisation and reliability of deep learning for digital pathology in a clinical setting

   Författare :Milda Pocevičiūtė; Claes Lundström; Stina Garvin; Gabriel Eilertsen; Nasir Rajpoot; Linköpings universitet; []
   Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; Deep learning; Digital pathology; Generalisation; Uncertainty estimation; Anomaly detection; Data distribution shift;

   Sammanfattning : Deep learning (DL) is a subfield of artificial intelligence (AI) focused on developing algorithms that learn from data to perform some tasks that can aid humans in their daily life or work assignments. Research demonstrates the potential of DL in supporting pathologists with routine tasks like detecting breast cancer metastases and grading prostate cancer. LÄS MER

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

   Författare :Karin Stacke; Jonas Unger; Gabriel Eilertsen; Claes Lundström; Henning Müller; Linköpings universitet; []
   Nyckelord :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

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

   Författare :Apostolia Tsirikoglou; Jonas Unger; Gabriel Eilertsen; Anders Ynnerman; Philipp Slusallek; Linköpings universitet; []
   Nyckelord :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