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Deep learning for automatic lung disease analysis in chest x-rays
Citation Link: https://doi.org/10.15480/882.3511
Publikationstyp
Doctoral Thesis
Date Issued
2021
Sprache
English
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2021-05-05
Institut
TORE-DOI
TORE-URI
Citation
Technische Universität Hamburg (2021)
This work addresses the automatic analysis of chest radiographs using Deep Learning, a rapidly evolving machine learning method. After a detailed evaluation of state-of-the-art CNN architectures on the ChestX-ray14 dataset, it is shown how models can be adapted to incorporate non-image features and improve their performance. Further, to simplify the appearance of CXRs and help CNNs deal with high-dimensional and limited data, as well as to improve their performance, a number of advanced pre-processing methods have been employed. Lastly, through simulations based on queuing theory and Markov processes, the significant clinical impact of smart worklist ordering is shown.
Subjects
deep learning
medical imaging
machine learning
X-ray
convolutional neural networks
DDC Class
610: Medizin
620: Ingenieurwissenschaften
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PhD_Thesis_IBa_TORE.pdf
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