Caitlyn J. Collins: Catalogue data in Spring Semester 2020

Name Dr. Caitlyn J. Collins
Address
Professur für Biomechanik
ETH Zürich, HCP H 11.3
Leopold-Ruzicka-Weg 4
8093 Zürich
SWITZERLAND
E-mailcaitlyn.collins@hest.ethz.ch
DepartmentHealth Sciences and Technology
RelationshipLecturer

NumberTitleECTSHoursLecturers
376-0022-00LImaging and Computing in Medicine Information Restricted registration - show details 4 credits3GR. Müller, P. Christen, C. J. Collins
AbstractImaging and computing methods are key to advances and innovation in medicine. This course introduces established fundamental as well as modern techniques and methods of imaging and computing in medicine.
Objective1. 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 fundamental 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 video lectures on the online learning platform Moodle. At the end of this first part, students must post a number of questions in the Moodle forum that will be addressed in the second part of the lectures using a flipped classroom concept. First, the lecturers may prepare additional teaching material to answer the posted questions and potentially discuss further questions (Q&A). Second, the students will form small groups to acquire additional knowledge online or from additionally distributed material and to present their findings to the rest of the class.
Lecture notesStored on Moodle.
Prerequisites / NoticeLectures will be given in English.