Index

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.~~

Generation of Clinical Text Reports from Chest X-Ray Images

Latent Diffusion Model for CT Synthesis

Latent diffusion model is a successful generative model in the modern computer vision researches. Modeling the generative process as image denoising, the diffusion models can generate realistic images in high quality and shows superior ability as the GAN-based models. In medical imaging, computed tomography (CT) is a well researched imaging modality and also widely applied in clinics. In this project, we will investigate the feasibility of modern diffusion models for the task of CT synthesis.

A Comparative Analysis of Loss Functions in Deep Learning-Based Inverse Problems

Introduction:

In recent years, deep learning has emerged as a transformative force in the realm of image processing, particularly in addressing inverse problems such as denoising and artifact reduction in medical imaging. This research aims to systematically investigate the impact of various loss functions on deep learning-based solutions for inverse problems, with a focus on low-dose Computed Tomography (CT) imaging.

Low-dose CT, while beneficial in reducing radiation exposure, often suffers from increased noise and artifacts, adversely affecting image quality and diagnostic reliability. Traditional denoising techniques, although effective to some extent, struggle to maintain a balance between noise reduction and the preservation of crucial image details. Deep learning, especially Convolutional Neural Networks (CNNs), has shown promising results in surpassing these traditional methods, offering enhanced image reconstruction with remarkable fidelity.

However, the choice of loss function in training deep learning models is critical and often dictates the quality of the reconstructed images. Commonly used loss functions like Mean Squared Error (MSE) or Structural Similarity Index (SSIM) have their limitations and may not always align well with human perceptual quality. This research proposes to explore and compare a variety of loss functions, including novel and hybrid formulations, to evaluate their efficacy in enhancing image quality, reducing noise, and eliminating artifacts in low-dose CT images.

Detectability Index Reimplementation for CT Images Using PyTorch

Work description
This project focuses on reimplementing the Detectability Index for evaluating individual CT projections, with the goal of improving the performance and adaptability of existing Python-based algorithms using PyTorch. The selected candidate will delve into the current code, identify performance bottlenecks, and propose innovative solutions to optimize efficiency. The goal is to minimize package dependencies to ensure code longevity and maintainability.

The following questions should be considered:

How can the existing Python code be improved with PyTorch for better performance and adaptability?
Where do the current code’s performance bottlenecks lie, and how can these be addressed?
How can the usage of external packages be minimized to ensure the code’s longevity?
What innovative approaches can be implemented to enhance the Detectability Index calculation?
How can the updated algorithm be validated for effectiveness and efficiency?

Prerequisites
Candidates should possess strong skills in Python and PyTorch, with the ability to quickly understand and improve upon existing code. A background in computational imaging or related fields, along with a problem-solving mindset, is essential.

For your application, please send your transcript of record.