Index
Evaluation of Reference-Free Registration Methods for Dynamic Vascular Roadmaps
Evaluation of the novel class of promptable image segmentation foundation models for radiotherapy tumor autosegmentation
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.