Exploring the lifecycle of artificial intelligence – deep learning for 1D to 4D biomedical data
type: CreativeWork
description: This thesis, titled "Exploring the Lifecycle of Artificial Intelligence - Deep Learning for 1D to 4D Biomedical Data" presents a comprehensive exploration of the Artificial Intelligence (AI) lifecycle as applied to various domains within the life sciences and medicine, specifically focusing on the application of Machine Learning (ML) and Deep Learning (DL) techniques to data ranging from one to four dimensional data. The study is structured around the AI lifecycle, which is elaborated upon in Chapter 2. This lifecycle encompasses the critical stages of problem definition, data acquisition, model training, evaluation, interpretation, and deployment, offering a holistic approach to the development and implementation of AI systems in medical contexts. A significant portion of the thesis delves into the interpretation of Electrocardiography (ECG) data, as detailed in Chapter 3. This chapter introduces the PTB-XL database, a large-scale dataset for ECG analysis, and discusses the application of eXplainable Artificial Intelligence techniques to enhance model transparency and trustworthiness. The study demonstrates how XAI can be employed to make AI models more interpretable and clinically relevant, which is crucial for their adoption in medical diagnostics. The thesis then shifts focus to the segmentation of 2D histological images, which is critical for accurate medical diagnostics, particularly in dermatology. As described in Chapter 4, this work explores advanced semantic segmentation techniques to identify and classify different biological structures within a Whole Slide Image (WSI). The chapter highlights the challenges and potential solutions in implementing DL models for this purpose, emphasizing the importance of precise segmentation in improving diagnostic accuracy. Further expanding on the dimensional complexity, Chapter 5 addresses the analysis of 3D neutrophil cell nuclei shapes using confocal fluorescence microscopy. This chapter provides insights into the cel…
abstract: This thesis, titled "Exploring the Lifecycle of Artificial Intelligence - Deep Learning for 1D to 4D Biomedical Data" presents a comprehensive exploration of the Artificial Intelligence (AI) lifecycle as applied to various domains within the life sciences and medicine, specifically focusing on the application of Machine Learning (ML) and Deep Learning (DL) techniques to data ranging from one to four dimensional data. The study is structured around the AI lifecycle, which is elaborated upon in Chapter 2. This lifecycle encompasses the critical stages of problem definition, data acquisition, model training, evaluation, interpretation, and deployment, offering a holistic approach to the development and implementation of AI systems in medical contexts. A significant portion of the thesis delves into the interpretation of Electrocardiography (ECG) data, as detailed in Chapter 3. This chapter introduces the PTB-XL database, a large-scale dataset for ECG analysis, and discusses the application of eXplainable Artificial Intelligence techniques to enhance model transparency and trustworthiness. The study demonstrates how XAI can be employed to make AI models more interpretable and clinically relevant, which is crucial for their adoption in medical diagnostics. The thesis then shifts focus to the segmentation of 2D histological images, which is critical for accurate medical diagnostics, particularly in dermatology. As described in Chapter 4, this work explores advanced semantic segmentation techniques to identify and classify different biological structures within a Whole Slide Image (WSI). The chapter highlights the challenges and potential solutions in implementing DL models for this purpose, emphasizing the importance of precise segmentation in improving diagnostic accuracy. Further expanding on the dimensional complexity, Chapter 5 addresses the analysis of 3D neutrophil cell nuclei shapes using confocal fluorescence microscopy. This chapter provides insights into the cel…
author: Wagner, Patrick
language: en
license: https://creativecommons.org/licenses/by-sa/4.0/
date published: 2025
date modified: 2026-07-27T21:28:49Z
provider: Technische Universität Berlin
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Technische Universität Berlin

PropertyValue
property ID: DOI
value: 10.14279/depositonce-23016