Master Assignment
[B] Surgical Phase Pattern Recognition from Video Data
Type: Master EE/CS/HMI
Period: TBD
Student: (Unassigned)
If you are interested, please contact :
Project Background:
Accurate identification of surgical phases from video footage is essential for improving surgical workflow analysis, enhancing training for medical professionals, and supporting real-time decision-making during operations. By recognizing patterns in surgical videos, AI can assist in understanding the sequence of tasks performed during surgery, leading to better surgical outcomes and optimization of operating room efficiency. This project focuses on developing advanced models to automatically recognize surgical phases from videos, enabling cutting-edge advancements in medical AI.
Project Overview:
- Develop machine learning models to recognize and classify distinct surgical phases from video data.
- Utilize computer vision techniques to analyze complex surgical patterns and identify the transitions between different phases.
- Address challenges such as occlusions, varying camera angles, and complex actions performed during surgeries.
- Contribute to medical applications by improving the understanding of surgical workflows for better training, patient outcomes, and operational efficiency.
Why Join?
- Gain hands-on experience with video understanding, using cutting-edge techniques in pattern recognition and machine learning.
- Collaborate with experts in AI and medical applications, contributing to research that enhances surgical precision and training.
Who Should Apply?
- Students with a background in machine learning and computer vision.
- Individuals eager to work on real-world challenges in the healthcare sector, using AI to enhance surgical phase recognition.