ID 2450: Incorporating Spatial Priors in Tissue Segmentation in Wound Images
Description:
This project focuses on developing a deep learning model that accurately segments tissues in a wound by incorporate prior knowledge about the wound structure using spatial priors. This targeted solution aims to streamline the tissue segmentation process, ultimately contributing to the SWODDYS project’s larger goals.
Tasks
- Literature Review: Conduct a deep literature review of using spatial priors based regularizers for Image Segmentation. and assess their applicability to wound tissue segmentation.
- Model Development & Validation: Develop, train, and validate a deep learning model using the available dataset. Compare with current state of the art models for Tissue Segmentation in Wounds.
- Comprehensive Documentation: Document all project steps, underlying assumptions, and acquired knowledge in a clear, concise, and organized manner in an IEEE conference paper format
- Regular Progress Updates: Maintain consistent communication by providing regular progress updates to supervisors.
Requirements:
- Technical Expertise: Possess strong experience in Deep Learning and Computer Vision.
- Programming Proficiency: Demonstrate proficiency in Python programming and libraries like PyTorch, NumPy,and OpenCV. Experience with Git and Slurm preferred.
- Communication Skills: Maintain a good command of the English language for effective written and verbal communication.
Application
- Deadline for applications: 01.11.2024. 10:00 am CEST
- Email me your CV and transcript of records.
- IMPORTANT! Examples of previous work.
- A simple explanation of a relevant publication from recent years related to the topic.
