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 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 CG7-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 CT3-Communicate in an effective way both orally and in writing, preparing documents and presenting projects and results with English language CG8-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 CT5-Collect and select information to be able to evaluate the state of the art of a specific topic or subject CE2-Learn which algorithm(s) could be more suitable in a particular application CE5-Ability to implement and evaluate artificial intelligence algorithms for the improvement of computer-assisted diagnosis, and critical ability to decide their daily clinical use CE9-Ability to implement and evaluate computer assisted detection algorithms, and critical ability to decide their daily clinical use CE38-Learn which algorithm(s) could be more suitable in a particular application CE46-Have a good knowledge of the field of computer-aided diagnosis (CADx) CE47- Have a general vision of the general characterization of the image CE48-Application of pattern recognition techniques in the field of medical images CE49-Know which features and which classifiers are most useful for different medical images CE50-Evaluation of a previously developed algorithm and estimation of its ease of use for medical images and daily clinical use. Estimate the crucial factors to make it a success
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
Activity type Hours with a teacher Hours without a teacher Virtual hours with a teacher Total Problem Based Learning (PBL) 16,00 53,00 0 69,00 Lecture / text commentary 6,00 22,00 0 28,00 Theory class 14,00 14,00 0 28,00 Total 36,00 89,00 0 125
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: Description of the activity Assessment Activity % Remediable subject Lecture Activity 50% document + 50% presentation and interaction 35 No Lab session: Traditional Diagnosis 70% strategy and results + 30% document 30 No Lab Project: Deep Learning Diagnosis 70% strategy and results + 30% document 35 Yes
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: Per considerar superada l’assignatura, caldrà obtenir una qualificació mínima de 5.0
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).