Index
Link Prediction on Utility Networks Using Graph Neural Networks
Abstract:
Utility network is a commonly used term for a collection of physical infrastructure components such as pipes, valves, pumps, etc., pipes that supply utilities like heat, water, electricity, and gas throughout the city. Structurally, the network of interconnected components can be replicated digitally with the help of GIS measurements. An accurate digital representation is crucial to maintain data integrity and ensure operational reliability. However, when physical components are digitally represented, measurement inaccuracies are introduced, diminishing the reliability of the digital model and impeding the process of deriving meaningful information. These inaccuracies often appear as missing pipes or disconnected networks due to translational errors. This thesis aims to formulate and tackle this problem as a graph based link (edge) prediction task using Deep Learning (DL), through Graph Neural Networks (GNNs). We apply the theory and methodology of SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction) [1], to the domain of utility networks. SEAL extracts local enclosing subgraphs around a link to learn patterns and predicts the existence of links. Additionally, we compare the effect of using pre-trained node2vec embeddings to embeddings learned simultaneously with the GNN model while experimenting with two different graph structures – homogeneous and heterogeneous-bipartite representations. We applied the methodology to real-world heat and water networks from Denmark. Overall, the pre-trained node2vec embeddings consistently outperformed those simultaneously learned with the GNN model. The optimal choice for the graph structure varied between the heat and water networks. Our experimentation on the heat network shows that heterogeneous-bipartite representation yielded better results, with an AUC score of 98% on the test set. In the case of the water networks, both the heterogeneous bipartite and the homogeneous representations produced comparable results, with an AUC score of 95%.
References:
This thesis is part of the “UtilityTwin” project.
Parkinson’s Disease Assessment Using Gait Analysis
Aphasia Assessment Using Deep Learning
Investigation of appropriate measures for the automated preparation and processing of modifications in diagnostic data for commissioning in vehicle production
Investigation of appropriate measures for the automated preparation and processing of modifications in diagnostic data for commissioning in vehicle production.
Metal Artifact Reduction in Computed Tomography Using Deep Learning Method
Building Knowledge Graphs from Legal Texts: Enhancing Decision Support with Applications in Formula 1
Legal documents, such as the FIA rulebook, are complex and difficult to navigate. Understanding these texts is time-consuming and prone to error. This thesis proposes using Natural Language Processing (NLP) and Knowledge Graphs (KGs) to transform legal texts into queryable, visual formats that simplify decision-making. Formula 1 will serve as a case study.
Objectives:
• Develop a pipeline to convert legal texts into navigable knowledge graphs.
• Create a queryable system for understanding relationships, exceptions, and dependencies.
• Detect inconsistencies and ambiguities in legal texts.
• Generalize the framework to apply to multiple legal domains. Approach
• Theoretical: Study legal text syntax/semantics, NLP techniques (e.g., BERT, GPT), and KG principles for modeling legal complexities.
• Practical:
o Build the KG using tools like Neo4j and visualize relationships between entities.
o Use ML algorithms to flag ambiguities or conflicts.
o Develop a natural language query interface for user-friendly interaction.
Generating Styled Handwritten Images based on Latent Diffusion Models
Handwriting generation is an important direction in computer vision and natural language processing.Traditional models such as AE, VAE, and GAN have gradually developed, while diffusion models have attracted much attention due to their better generation quality and stability. At present, advanced methods include GANwriting, which extracts writing styles through style encoders and generates texts with matching styles and accurate content; VATr++ combines visual perception modules with Transformer architecture, and uses multi-level conditional control and hybrid attention mechanisms to achieve accurate imitation of complex handwriting styles; WordStylist combines semantic information and style features based on the latent diffusion model (LDM) to generate texts with accurate styles; DiffusionPen generates handwritten images by denoising in the latent space through content encoding and style encoding to ensure consistency of content and style; DiffCJK combines conditional diffusion models and language-specific knowledge to achieve high-quality generation with excellent local details and global structures based on the characteristics of Chinese, Japanese, and Korean characters. Although LDM performs well in generation quality and multi-language support, it still faces challenges such as insufficient efficiency, poor adaptability to few samples, and limited style diversity.
In this work, I want to generate Styled handwritten images based on LDM at the word level to further improve the generation efficiency, enhance the style generalization ability, and achieve more refined style control.
The implementation will be done in Python / Pytorch.
The thesis consists of the following milestones:
– Explore techniques to accelerate the diffusion process (e.g., fast sampling algorithms or segmented denoising strategies) to reduce generation time.
– Optimize the representation of the latent space to further reduce the computational complexity.
– Try using different mechanisms or enhanced learning methods to improve adaptability to extreme styles
– Further experiments and optimizations on the learning process and network architecture.
– Evaluate performance and compare with other new technologies
Leveraging Foundational Models for Segmentation Tasks in Coronary Angiography
Automatic Delineation of the Radiotherapy Clinical Target Volume in Brain Tumour Patients
Denoising and Inpainting of 3D OCT images using Deep Learning
As a non-invasive 3D optical imaging modality that operates on micrometer-scale, Optical Coherence Tomography (OCT) has become a standard of care in ophthalmology [1].
However, OCT imaging in general is a noisy process, with two of the typical noise sources being detection noise and laser speckle [2], [3]. There are multiple approaches for image enhancement. Due to the lack of ground truth data, deep learning approaches are often unsupervised. Noise2Noise [4] learns a denoising operation on images without actually needing clean versions of the samples during the training step. Instead, they use assumptions about the statistical nature of noise compared to actual data [4], [3]. An example where deep learning has been employed to improve OCT-related data before, is given in [3]. This work is primarily optimized for a low latency scenario and works by employing an unsupervised blind-spot denoising network that is trained on a masked version of the original data. A more complex approach to generate high quality data is volume fusion. Volume fusion is a 3 step process, which is comprised of motion correction of multiple OCT images, e.g., [6], illumination correction of brightness artifacts, e.g., [7], and merging of the resulting data. Results in [5] demonstrate signal enhancement and improve visibility of subtle retinal features on a micrometer scale. However, the authors of [5] suggest to use around 4–6 volumes for clean results. While using a lower number of images would be preferable for efficient clinical screening, using only two volumes could lead to gaps in the resulting image. Gaps result from eye motion during the OCT scanning process. Thus, it would be preferable to have an option to improve the results when using fewer scans, but still achieve levels of image quality similar to using more volumes.
The goal of this master’s thesis is to develop a method for denoising and inpainting of gaps in motioncorrected 3D-OCT images using supervised deep learning. We aim to improve the quality of images fused from fewer scans by training a denoiser with high quality scans that were combined and aggregated, using [6] and [7] as ground truth for our training.
The results will then be evaluated accordingly. Possible metrics for the evaluation of such a method could be structural similarity, peak signal-to-noise ratio or the contrast to noise ratio between the resulting image and the ground truth. Additionally, the correctness of inpainting will be evaluated by comparing the result to additional co-registered data that was not available to the image enhancement method.
In addition, this master’s thesis has the following requirements:
– literature research
– assembling of training and test sets with healthy data as well as data with different pathologies
– implementation of the method using a common deep learning framework
– submission of the method and the evaluation code
– Description of the performed work in a written thesis according to the lab’s thesis guidelines
– introductory and final presentation
References:
[1] Fujimoto J, Swanson E. “The Development, Commercialization, and Impact of Optical Coherence
Tomography.” In: Invest Ophthalmol Vis Sci. 2016 Jul 1;57(9):OCT1-OCT13, doi: 10.1167/iovs.16-19963.
PMID: 27409459; PMCID: PMC4968928.
[2] DuBose, Theodore B., et al.” Statistical models of signal and noise and fundamental limits of
segmentation accuracy in retinal optical coherence tomography.” In: IEEE transactions on medical
imaging, 2017, 37. Jg., Nr. 9, S. 1978-1988.
[3] Nienhaus, J., Matten, P., Britten, A. et al. “Live 4D-OCT denoising with self-supervised deep learning.”
In: Sci Rep 13, 5760 (2023), doi: 10.1038/s41598-023-32695-1
[4] Lehtinen, J. Noise2Noise: Learning Image Restoration without Clean Data. arXiv preprint
arXiv:1803.04189, 2018.
[5] Won, Jungeun, et al. “Topographic Measurement of the Subretinal Pigment Epithelium Space in
Normal Aging and Age-Related Macular Degeneration Using High-Resolution OCT.” In: Investigative
Ophthalmology & Visual Science, 2024, 65. Jg., Nr. 10, S. 18-18.
[6] Ploner, Stefan, et al. “A spatiotemporal model for precise and efficient fully-automatic 3d motion
correction in oct.” In: International Conference on Medical Image Computing and Computer-Assisted
Intervention. Cham: Springer Nature Switzerland, 2022. S. 517-527, doi: 10.1007/978-3-031-16434-7_50
[7] Ploner, Stefan, et al. “A spatiotemporal illumination model for 3d image fusion in optical coherence
tomography.”, In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). IEEE, 2023. S. 1-
5., doi: 10.1109/ISBI53787.2023.10230526.