Search for dissertations about: "machine diagnostics"

Showing result 11 - 15 of 32 swedish dissertations containing the words machine diagnostics.

  2. 11. Computational modelling in systems biology: from rewriting cell fates to detecting tumours

    Author : Emil Andersson; Centrum för miljö- och klimatvetenskap (CEC); []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Beräkningsmodeller; Systembiologi; Genreguleringsnätverk; Cellomprogrammering; Energilandskap; iPSC; Agentbaserad modellering; T-cellsutveckling; Maskininlärning; Tumördiagnostik; Computational modelling; Systems biology; Gene regulatory network; Cell reprogramming; Energy landscape; iPSC; Agent-based modelling; Multi-scale modelling; T-cell development; Machine learning; Tumour diagnostics;

    Abstract : Computational modelling is becoming an increasingly important tool in biological research. By performing computer simulations of models, it becomes possible to test theories about a biological system against experimental data. Simulations can also be used as a replacement for experiments otherwise unattainable. READ MORE

  4. 12. Accelerators for Physics Experiments : From Diagnostics and Control to Design

    Author : Elena Wildner; Thomas Lindblad; Mikael Eriksson; KTH; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Astroparticle physics; Astropartikelfysik;

    Abstract : This thesis develops techniques of control-methods, optimization, and diagnostics of accelerator equipment and the produced particle beams with emphasis on the Large Hadron Collider (LHC) project at CERN. From a solid knowledge of the characteristics of the manufactured accelerator equipment gained from in-depth measurements and analysis of measured data, a link to an enhanced equipment design can be made. READ MORE

  5. 13. Development and application of rule- and learning-based approaches within the scope of neuroimaging : Tensor voting, tractography and machine learning

    Author : Daniel Jörgens; Rodrigo Moreno; Örjan Smedby; Chunliang Wang; Jesper Andersson; KTH; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; tensor voting; tractography; deep learning; tractogram filtering; diffusion magnetic resonance imaging; tensorröstning; traktografi; djupinlärning; traktogramfiltrering; diffusions-MRT; Tillämpad medicinsk teknik; Applied Medical Technology;

    Abstract : The opportunity to non-invasively probe the structure and function of different parts of the human body makes medical imaging an indispensable tool in clinical diagnostics and related fields of research. Especially neuroscientists rely on modalities like structural or functional Magnetic Resonance Imaging, Computed Tomography or Positron Emission Tomography to study the human brain in vivo. READ MORE

  6. 14. Machine Learning methods in shotgun proteomics

    Author : Patrick Truong; Lukas Käll; Peter Nilsson; KTH; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; mass spectrometry protein summarization Bayesian hierarchical modelling label-free quantification data-independent acquisition mass spectrometry; benchmark mathematical methods; transformers; computational proteomics; proteomics; bioinformatics; bert; ms2 intensity; probabilistic modelling; Biotechnology; Bioteknologi;

    Abstract : As high-throughput biology experiments generate increasing amounts of data, the field is naturally turning to data-driven methods for the analysis and extraction of novel insights. These insights into biological systems are crucial for understanding disease progression, drug targets, treatment development, and diagnostics methods, ultimately leading to improving human health and well-being, as well as, deeper insight into cellular biology. READ MORE

  7. 15. Deep Learning for Digital Pathology in Limited Data Scenarios

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

    Abstract : 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. READ MORE