General information |
Course unit name: Deep Learning for Medical Image Analysis
Course unit code: 575047
Academic year: 2021-2022
Coordinator: Simone Balocco
Department: Faculty of Mathematics and Computer Science
Credits: 3
Single program: S
Estimated learning time |
Total number of hours 75 |
Face-to-face and/or online activities |
32 |
(Due to the Covid-19 restrictions, we expect to have 50%-75% of in-person activities) |
Independent learning |
43 |
Learning objectives |
Referring to knowledge . |
Teaching blocks |
1. Introduction to the clinical image modalities.
* - CT,
- MRI,
- US,
- X-ray,
- dermatoscopy,
2. Techniques for data analysis
* - Segmentation,
- Registration,
- object detection,
- De-noising techniques for data analysis
3. Neural network for medical imaging
* - 2D,
- 3D,
- Temporal analysis,
- Gans for data augmentation
4. Data annotations and Unbalanced labelling.
5. Data bases and challenges
Teaching methods and general organization |
Teaching will follow a face-to-face (in-person), virtual (online) or mixed model according to the instructions of the competent authorities. In principle, we expect to follow the mixed teaching model for the 2021-22 academic year. |
Official assessment of learning outcomes |
Depending on the health situation, the evaluable activities can be: face-to-face tests, synchronous online tests or work delivery.
Examination-based assessment Depending on the health situation, the evaluable activities can be: face-to-face tests, synchronous online tests or work delivery. |
Reading and study resources |
Consulteu la disponibilitat a CERCABIB
Book
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88. https://doi.org/10.1016/j.media.2017.07.005
Greenspan, H., Van Ginneken, B., & Summers, R. M. (2016). Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging, 35(5), 1153-1159. https://ieeexplore.ieee.org/abstract/document/7463094/