Estudia

• CG3- Communicate in an effective way both orally and in writing, preparing documents and presenting projects and results with English language
• CG5- Collect and select information to be able to evaluate the state of the art of a specific topic or subject
• CB7- That students know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader contexts related to their area of ??study
• CB8- That students are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments
• CE1- Programming, at an advanced level, in the languages and libraries most used in intelligent field robotics
• CE2- Analyse a problem related to intelligent autonomous systems and identify the appropriate techniques and tools to solve it
• CE3- Understand, develop, modify and effectively apply machine learning methods.
• CE7- Understand and be able to apply the main computer-based perception techniques
• CE8- Understand the mathematical foundations of intelligent robotic system algorithms

• To have a good knowledge of the field of Computer Aided Diagnosis (CADx).
• To have an overview of general image characterization.
• Applying pattern recognition techniques to the field of medical imaging.
• To learn what characteristics and what classifiers are more useful to the different medical images.
• To be able to evaluate a previously developed algorithm and asses is usability for medical images and daily clinical usage. Estimate the crucial factors for it to be successful.
• To learn what algorithm(s) could fit better for a particular application.

1. Introduction to diagnosis and CADx

2. Image characterization: morphological, texture, and shape descriptors

3. Interest point detectors and descriptors

4. Object and image characterization

5. Deep Learning for classification

6. Deep Learning applications

7. CADx evaluation and applications

• Isaac Bankman (2008). Handbook of Medical Imaging: Processing and Analysis. Elsevier.
• Forsyth, David A, Ponce Jean (2003). Computer vision : a modern approach. Upper Saddle River: Prentice Hal.
• P. Suetens (2002). Fundamentals of Medical Imaging. Cambridge University Press.
• A. Dhawan (2010). Medical Image Analysis. Wiley. 2nd Edition .

Assessment activities:

The evaluation is based on three different activities: 30% from the fist Lab assignment + 35 % from final Lab Project + 35% by evaluating the lectures given by the students.

If fraudulent actions are detected in any type of academic activity (use of information without authorization, use of false information, use of unauthorized devices, impersonation, total or partial plagiarism, purchase and sale of tests, practices and assignments, etc) the students involved will automatically fail the subject. Depending on the type of fraudulent act, the School Management will initiate the appropriate procedures in accordance with Law 3/2022 of February 24 on University Coexistence (https://www.boe.es/eli/es/l/2022/02/24/3)

Specific criteria for the "No show" grade:
NP will be considered when the students do not submit any of the evaluation activities (P1, P2, Final project, or Lecture activity)

Single Assessment:
Students should do the final project and an exam related to the contents seen in the course

Minimum requirements to pass:
To pass the course, a minimum mark of 5.0 should be achieved.

To stablish the appointments students can use moodle or the emails. These appointments can be done online via google meet meetings or in person (office 0.15 P4 Building)

The communitation and interaction with the student will be mainly done via moodle, having also specific forums for all the activities.

Students can also interact with the teacher via email or via videoconferences (google meet).

Mentoring will be held in the office 015 of building P-IV.