Imaging and Computing in Medicine

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Imaging and Computing in Medicine (ICM) is a highly innovative, cross-​disciplinary course bringing together students from 7 departments (HEST, ITET, MAVT, PHYS, BIOL, CHAB, GESS) that uses state-​of-the-art educational techniques to enhance students’ understanding of imaging fundamentals and computational methods in medicine. The course content is highly relevant for helping students improve their problem-​solving abilities in a wide range of contexts. As ICM is one of the few courses offered to D-​HEST students (84% of class) with a major programming component, it allows them to write their code, while exploring various medical imaging modalities, image processing, computational modeling, and computer vision techniques.

In response to Covid-​19, the course transitioned within a week from in-​presence to online Q&A sessions and activities with the possibility for students to interact live with the lecturer. Instead of using a computer lab or personal computers where the required software would need to be installed on over 100 computers, the activities were run online using a server accessible through any browser with ETH authentication. Here, the students interact with and modify Jupyter Notebooks which provide live python code, means of visualization, and instructions for activities that are designed to require collaboration amongst students. Tutors provide personalized assistance in small breakout rooms in zoom and classroom assessment techniques (CATs, e.g. aha slides, Moodle workbooks) are used to test students’ understanding and receive feedback on the clarity of the activities in real-​time.

Implementation of the course during the time of distance learning

ICM continuously adapted and improved during the time of distance learning and is now structured as a three-​part
seminar series:

  • First, students study the fundamentals in short video lectures designed to follow the ETH TORQUE (Tiny, Open-​with-Restrictions courses focused on QUality and Effectiveness) guidelines. These lectures are designed to convey the learning objectives as clearly and concisely as possible. Students are encouraged to explore each topic further and post questions directly within the comments section of the video lectures.
  • Second, the lecturers prepare additional teaching material to address posted questions and discuss this additional material with the students in an interactive online classroom in a question & answer session.
  • Third, in-​class activities are introduced using an interactive slide format (aha slides) with built-​in CATs. The class is split into breakout rooms where students work togETHer in teams of eight on the programming project with the supervision of teaching assistants. In this collaborative atmosphere, the cross-​disciplinary teams of students learn widely applicable programming skills and develop innovative solutions to problems in the field of medicine. Tutors circulate between breakoutrooms, answering questions and engaging students.

The following innovative and effective methods implemented in ICM can conveniently be translated to other lectures:

  • The concise lecture videos are implemented using the Interactive Video Suite on ETH Moodle. The comments section encourages and incentivizes to post questions and links individual questions to the relevant slide/video time-​point. Students are also able to reply to comments of others and therefore have higher interaction amongst themselves in a blended learning environment. On average, students ask over 1000 content-​related questions throughout the course.
  • The live Q&A, flipped classroom style, explores topics of interest to students, enhancing clarity and engagement. The balance between at-​home and in-​class work provides the students with time to reflect on the learning objectives and revisit areas in which they may lack confidence. • The Jupyter Notebook activities and cross-​disciplinary teamwork help students develop problem-​solving skills and do not require local software installations. All work can be conducted via a browser and in breakout rooms in Zoom.
  • Breakout rooms of 6-8 people with tutors directly engaging the students by looking in real-​time at their solution attempts on Moodle and in the Jupyter server as well as Muddiest Point and Application Card CATs.
  • Activities designed to encourage collaboration and discussion within breakout rooms. Specifically, our activities were divided in sections, each providing only part of the answers to a set of questions. Within the breakout room students needed to divide the work amongst each other, then discuss and analyze results all together.
  • Productive failure: Students were first asked to solve problems then provided with the complete info needed. This learning model proved effective with activity scores higher than in previous years and more questions asked while explaining the theoretical background behind the solution procedure. In addition to the many positive comments from students, student retention, attendance, participation, and performance are high despite Covid.

Overall concept of the course before the pandemic – during – after

As noted, ICM is a relatively new course. In the first semester (spring 2019), the course was conducted exclusively in presence with lectures in a lecture hall and activities on student’s personal computers. In the spring of 2020, we started the semester much the same until covid measures were put in place. The shift from in-​person to online needed to be executed rapidly. Within a week, the Q&A sessions and activities were being hosted online, with the possibility for students to interact live with the lecturer and receive personalized assistance from tutors in small breakout rooms. The feedback from students was that the transition after Covid-​19 was very smooth. We also switched to entirely online activities using a student server that students adapted to very quickly.

After the initial major changes to the course design, we began more gradual optimization work focused on improving students’ experience and performance independent of the pandemic.

  • We implemented a variety of classroom assessment techniques (CATs) such as mentimeter slides, aha slides, and Moodle quizzes to test students’ understanding and receive feedback on the clarity of the activities in real-​time.
  • We also adapted the size of our zoom breakout rooms from approximately 20 with a permanent tutor in each room to 6 to 8 per breakout room with tutors switching between breakout rooms to achieve the optimal mix between student collaboration and student-​tutor interactions. This also encouraged student collaboration and discussion by making activity problems much more easily solvable through the division of labor within each breakout room.
  • Learning from a recent study at ETH, we implemented productive failure elements in our activities, encouraging students to find answers to problems on their own before giving them access to the background info and our suggested solution procedure.

Course Description

Imaging and Computing in Medicine
Imaging 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.
The 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.
376-​0022-00 G
Bachelor & Master
Lectures, discussions, exercises
session examination