376-0022-00L  Imaging and Computing in Medicine

SemesterSpring Semester 2022
LecturersR. Müller, C. J. Collins
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
376-0022-00 GImaging and Computing in Medicine Special students and auditors need a special permission from the lecturers.4 hrs
Tue12:45-15:30HCI G 7 »
13:45-15:30HIT F 32 »
13:45-15:30HIT J 51 »
13:45-15:30HIT J 53 »
13:45-15:30HIT K 51 »
13:45-15:30HIT K 52 »
R. Müller, C. J. Collins

Catalogue data

AbstractImaging and computing methods are key to advances and innovation in medicine. This course introduces established fundamentals as well as modern techniques and methods of imaging and computing in medicine.
Learning objectiveThe learning objectives include 1. Understanding and practical implementation of biosignal processes methods for imaging; 2. Understanding of imaging techniques including radiation imaging, radiographic imaging systems, computed tomography imaging, diagnostic ultrasound imaging, and magnetic resonance imaging; 3. Knowledge of computing, programming, modelling and simulation fundamentals; 4. Computational and systems thinking as well as scripting and programming skills; 5. Understanding and practical implementation of emerging computational methods and their application in medicine including artificial intelligence, deep learning, big data, and complexity; 6. Understanding of the emerging concept of personalised and in silico medicine; 7. Encouragement of critical thinking and creating an environment for independent and self-directed studying.
ContentImaging and computing methods are key to advances and innovation in medicine. This course introduces established fundamentals as well as modern techniques and methods of imaging and computing in medicine. For the imaging portion of the course, biosignal processing, radiation imaging, radiographic imaging systems, computed tomography imaging, diagnostic ultrasound imaging, and magnetic resonance imaging are covered. For the computing portion of the course, computing, programming, and modelling and simulation fundamentals are covered as well as their application in artificial intelligence and deep learning; complexity and systems medicine; big data and personalised medicine; and computational physiology and in silico medicine.
The course is structured as a seminar in three parts of 45 minutes with video lectures and a flipped classroom setup. In the first part (TORQUEs: Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness), students study the basic concepts in short, interactive video lectures on the online learning platform Moodle. Students are able to post questions at the end of each video lecture or the Moodle forum that will be addressed in the second part of the lectures using a flipped classroom concept. For the flipped classroom, the lecturers may prepare additional teaching material to answer the posted questions (Q&A). Following the Q&A, the students will form small groups to acquire additional knowledge using online, python-based activities via JupyterHub or additionally distributed material and discuss their findings in teams. Learning outcomes will be reinforced with weekly Moodle assignments to be completed during the flipped classroom portion.
Lecture notesStored on Moodle.
Prerequisites / NoticeLectures will be given in English.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
In examination block forBachelor's Programme in Health Sciences and Technology 2011; Version 01.08.2016 (Examination Block 4)
ECTS credits6 credits
ExaminersR. Müller, C. J. Collins
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationExams will be conducted on the computer in a session examination. Additionally, students can receive a bonus of 0.25 grade points towards the final exam grade for online participation in the interactive videos in the TORQUES. The maximum grade 6 for the lecture can also be achieved if only the session exam is completed. In the case of a possible examination repetition, the performance during the course is taken over by default. If this is not desired, the lecture must be retaken.
Written aidsEnglish Dictionary
Digital examThe exam takes place on devices provided by ETH Zurich.
If the course unit is part of an examination block, the credits are allocated for the successful completion of the whole block.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkCourse related material
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

General : Special students and auditors need a special permission from the lecturers

Offered in

ProgrammeSectionType
Electrical Engineering and Information Technology BachelorEngineering ElectivesWInformation
Health Sciences and Technology BachelorMedical TechnologyWInformation
Health Sciences and Technology BachelorMedical TechnologyWInformation
Health Sciences and Technology MasterElective CoursesWInformation
Human Medicine BachelorCompensatory CoursesWInformation
MAS in Medical PhysicsCore CoursesWInformation
MAS in Medical PhysicsElectivesWInformation
Mechanical Engineering BachelorEngineering for HealthWInformation
Pharmaceutical Sciences BachelorCompensatory CoursesWInformation
Science, Technology, and Policy MasterLife Science and HealthWInformation