Development of generalizable DL models for gastrointestinal disease segmentation
We aim to create and benchmark a gastrointestinal tract and instruments datasets for automatic segmentation task. To address the demand for the annotated medical dataset, we will create a new dataset with the help of medical experts and make it publicly available to the multimedia community. Additionally, we will provide the benchmark algorithm and the parameters that can be used to test and compare the algorithms against each other. We will further analyze the results of MedicoTask 2018 and try to improve the results.
To develop a fully automated system for gastrointestinal tract disease segmentation.
- Deep understanding of semantic segmentation based approach
- Working on a real-world application
- Possibility of collaboration with researchers
- Possibility to implement and research a novel approach
- Opportunity to participate in challenges and conferences
- Experience with Python programming
- Understanding of machine learning
- Experience with Keras and Tensorflow
- Pål Halvorsen
- Michael Riegler
- Debesh Jha
Simula Metropolitan Center For Digital Engineering AS
Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
Allan, M., Shvets, A., Kurmann, T., Zhang, Z., Duggal, R., Su, Y. H., ... & Herrera, L. (2019). 2017 Robotic instrument segmentation challenge. arXiv preprint arXiv:1902.06426.