Development of diagnosis support system for endoscopic images using machine learning
Colonoscopy is the gold standard for colon examinations. The factors such as gaps among endoscopists skill and experience and quality of bowel preparation can lead to the miss-detection of the lesions. The other challenging factors are related to the structure and characteristics of lesions (for example, size, color, shape, and occurrence), making the detection more difficult. One of the solutions could be the use of machine learning to detect such diseases. However, machine learning algorithms require many samples for the training and development of the algorithms. In this respect, we will develop a new dataset with the help of medical experts and make it publicly available to the multimedia community. Later on, we will use the same dataset and develop new algorithms and provide a new benchmark.
To develop a fully automated system for endoscopic disease segmentation, detection, and localization
Deep understanding of endoscopic image analysis
Working on a real-world application
Possibility of collaboration with researchers
Opportunity to implement and research a novel approach
Opportunity to participate in challenges and conferences
Experience with Python programming
Understanding of deep learning will be a benefit.
- Pål Halvorsen
- Michael Riegler
- Debesh Jha
Simula Metropolitan Center For Digital Engineering AS
- D. Jha, M. A. Riegler, D. Johansen, P. Halvorsen, and H. D. Johansen“ DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation,” CBMS, 2020.
- Sharib Ali et al., “An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy”, Scientific Reports, 2020.