Index

Defect Detection Probability as a Metric for CT Image Quality Assessment

This project focuses on using defect detection probability within CT (Computed Tomography) images as a metric for assessing image quality. Key steps include:

Establishing a data preparation pipeline to insert defects into CT volumes sourced from CAD files.
Simulating CT scans to replicate imaging processes.
Developing a defect detection neural network to analyze CT images and determine the probability of defect presence.
Utilizing the defect detection probability as a quantitative metric for evaluating the quality of CT images, with potential integration of trajectory optimization techniques.

Automated ONNX2TikZ: Generating LaTeX-TikZ Diagrams of Neural Networks

This project aims to automate the conversion of ONNX models into TikZ code, facilitating the creation of visually appealing diagrams in LaTeX documents. Leveraging Python for ONNX parsing and manipulation, alongside LaTeX and TikZ for rendering, this tool streamlines the process of visualizing neural network architectures for academic papers, presentations, and educational materials

Feature Extraction and Dimensionality Reduction Techniques for Assessing Model Similarity in Large-Scale 3D CAD Datasets

Work description
The research presented in this thesis explores the application of feature extraction and dimensionality reduction techniques to assess model similarity within large-scale 3D CAD datasets. It investigates how different geometric and topological descriptors can be quantified and utilized to measure the similarity between complex 3D models. Therefore, the study employs advanced machine learning algorithms to analyze and cluster 3D data, facilitating a better understanding of model characteristics and relationships.

During the thesis, the following questions should be considered:

What metrics can effectively quantify the variance in a training dataset?
How does the variance within a training set impact the neural network’s ability to generalize to new, unseen data?
What is the optimal balance of diversity and specificity in a training dataset to maximize NN performance?
How can training datasets be curated to include a beneficial level of variance without compromising the quality of the neural network’s output?
What methodologies can be implemented to systematically adjust the variance in training data and evaluate its impact on NN generalization?

Prerequisites
Applicants should have a solid background in machine learning and deep learning, with strong technical skills in Python and experience with PyTorch. Candidates should also possess the capability to work independently and have a keen interest in exploring the theoretical aspects of neural network training.

For your application, please send your transcript of record.

Review of Zero-shot, Few-shot classification, detection and segmentation methods in Medical Imaging

Review of Zero-shot, Few shot classification, detection and segmentation methods in medical imaging.

Evaluation of MedKLIP for Zero-shot and Fine-tuned classification of CXRs

Zero-shot scores on NIH and RSNA Pneumonia datasets. Analysis of attention maps and point score on VinDR-CXR dataset. Analysis of performance improvement from zero-shot to fine-tuned classification performance for various findings.

Center-to-Peer Federated Learning Research

Generating High-Resolution CT Images via Score-Based Diffusion and Super-Resolution Techniques

Partial Convolution for CT Field of View Extension

Diffusion Model-Enabled Energy Level Transformation in Photon Counting Computed Tomography (PCCT)

Introduction:

Photon counting computed tomography (PCCT) marks a new era in medical imaging, offering an unprecedented ability to discriminate between different photon energy levels. This feature of PCCT is crucial for enhancing image contrast and specificity, allowing for more accurate tissue characterization. However, efficiently managing and converting between these diverse energy levels in a clinically practical manner remains a significant challenge.

This project aims to utilize diffusion model to streamline and optimize the energy level conversion process in PCCT. By leveraging the advanced pattern recognition and computational capabilities of diffusion model, the project intends to develop a system that can automatically and accurately translate between different photon energy levels, enhancing the utility and clarity of PCCT images.

The ultimate goal is to provide a robust and efficient framework that not only improves the diagnostic quality of PCCT images but also expands the practical applications of this technology in clinical settings. This involves intricate work in both the development of diffusion model and the understanding of the physics underlying PCCT.

Requirements:

Completion of Deep Learning is mandatory.
Proficiency in PyTorch is essential.
Strong analytical and problem-solving skills.

Prospective candidates are warmly invited to send their CV and transcript to yipeng.sun@fau.de.