Teaching plan for the course unit

 

 

Close imatge de maquetació

 

Print

 

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

More information enllaç

 

 

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.

* In case of in-person teaching:

The weekly schedule of in-person activities is distributed in three hours of theory class that includes practice.

* In case of mixed teaching required by the health situation (this is the expected model):

If the health situation allows it and the necessary conditions are met, we expect to have between 50% and 70% of in-person activities. In general, when having an occupancy rate of 50%, students will attend in-person for a week and will follow class on streaming for the following week.

For virtual teaching, material will be delivered so that students can consult it asynchronously.

For in-person teaching, the time will be devoted to Questions and Answer sessions regarding the theory material or regarding the practical exercises. Priority will also be given to carrying out evaluation activities in person.

Moreover, synchronous online sessions will be scheduled to keep the proper subject dynamics and / or the resolution of doubts that may arise (and that complement the raised in in-person activities).



* In case on-line teaching is required by the health situation:

The time ranges of mixed teaching are maintained but all teaching will be carried out in an online format.

As far as possible, the gender perspective will be incorporated in the development of the subject. In addition, teachers will be attentive to those specific gender needs that students may raise, such as being able to choose a partner of the same gender if group work is carried out or being able to pose challenges against the gender gap.

 

 

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.

The course will follow a continuous evaluation consisting in practical reports (PR) and in-class presentations (PS). A test (or multiple mini-tests) about the theory will be performed (TS). The final score (FS) will be computed as follows:
FS = 0.4 * PR + 0.3 * PS + 0.3 * TS
A minimum score of 3 over 10 points is required for each part PR, PS, and TS in order to compute the final score FS.

 

Examination-based assessment

Depending on the health situation, the evaluable activities can be: face-to-face tests, synchronous online tests or work delivery.

The course will follow a continuous evaluation consisting in practical reports (PR) and in-class presentations (PS). A test (or multiple mini-tests) about the theory will be performed (TS). The final score (FS) will be computed as follows:
FS = 0.4 * PR + 0.3 * PS + 0.3 * TS
A minimum score of 3 over 10 points is required for each part PR, PS, and TS in order to compute the final score FS.

 

 

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/