eprintid: 36531
rev_number: 40
eprint_status: archive
userid: 8999
dir: disk0/00/03/65/31
datestamp: 2025-05-22 07:25:41
lastmod: 2025-06-10 07:19:32
status_changed: 2025-05-22 07:25:41
type: doctoralThesis
metadata_visibility: show
creators_name: Eulig, Elias
title: Challenges and Opportunities of Deep Learning in X-Ray Imaging and Computed Tomography
divisions: i-130001
divisions: i-850300
adv_faculty: af-13
abstract: Deep learning has revolutionized medical imaging by providing state-of-the-art solutions for a wide range of tasks. However, the use of deep learning in medical imaging also comes with its own set of challenges, three of which are addressed in the projects presented in this cumulative thesis.
The first project focuses on the fair evaluation of deep learning-based low-dose computed tomography (LDCT) image denoising algorithms by introducing a novel benchmark framework. The second project addresses the interpretability and robustness of deep learning algorithms for LDCT image denoising by investigating their invariances (i.e., which features in the images they learned to represent and which to ignore). The third project tackles the scarcity of data for deep learning-based digital subtraction angiography (DSA) by simulating paired training data.
The proposed methods are capable of overcoming the respective challenges associated with deep learning in medical imaging and through this could enable the development of better and safer algorithms for clinical practice.
date: 2025
id_scheme: DOI
id_number: 10.11588/heidok.00036531
ppn_swb: 1927537142
own_urn: urn:nbn:de:bsz:16-heidok-365311
date_accepted: 2025-05-07
advisor: HASH(0x55602a853688)
language: eng
bibsort: EULIGELIASCHALLENGES20250507
full_text_status: public
place_of_pub: Heidelberg
citation:   Eulig, Elias  (2025) Challenges and Opportunities of Deep Learning in X-Ray Imaging and Computed Tomography.  [Dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/36531/1/Dissertation_EE.pdf