Index

Enhancing Inference Efficiency of Deep Learning Models for Camera-Based Road Segmentation

ISLES Challenge 2024: Infarct segmentation from CT images

Final, post-treatment infarct segmentation from pre-treatment acute imaging (CT) and clinical data.

Idea: Investigate data from this year’s ISLES 2024 challenge and build + train a model for stroke lesion segmentation. Potentially submit the model to the challenge.

Reference: https://isles-24.grand-challenge.org/

 

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

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.