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Vorlesungsverzeichnis >> Fakultät Wirtschaftsinformatik und Angewandte Informatik >>
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xAI-Sem-B1: Bachelorseminar Erklärbares Maschinelles Lernen- Dozentinnen/Dozenten
- Sebastian Dörrich, M.Sc., Prof. Dr. Christian Ledig
- Angaben
- Seminar
Rein Präsenz
2,00 SWS
Zeit und Ort: Mi 14:00 - 16:00, WE5/03.004
- Voraussetzungen / Organisatorisches
- Interest and registration
If you have questions or want to express interest, please send an Email with name and matriculation number to sebastian.doerrich@uni-bamberg.de. Registration via central VC course
Requirements:
none
- Inhalt
- Topic: Medical Imaging
Motivation: Medical imaging is used to support the diagnosis and treatment of diseases without the need for surgery or other intrusive measures.
Widely used imaging modalities enable the visualization of the interior of the human body, which consequently allows doctors to assess a patient's anatomy including bones, organs, tissue, and blood vessels through non-invasive means.
Hence, medical imaging can be used to detect a disease, help determine whether surgery is needed, locate tumors, find blood clots and other blockages, or assist doctors during interventions.
Acquired images can be collected and grouped together to create pathology-specific databases for the differentiation of abnormalities from normal anatomy, or the development of new procedures and approaches.
In a variety of research efforts, this often requires that patient data is made publicly available.
However, since this type of data is extremely sensitive, its storage and usage are thus restricted which in return poses a key challenge current researchers must face every day when working with medical data.
Topics can cover different aspects of the imaging process, including physics of image acquisitions (X-ray, MR, US, ...), reconstruction algorithms (e.g., backprojection), data storage formats (2D vs 3D, DICOM), modality specific benefits and challenges, etc. As such you can get answers to the following practical questons by attending the seminar:
What is medical imaging? How does Computed Tomography (CT) work?; When should we do an X-Ray scan rather than a CT or MRI?; When could Magnetic Resonance Imaging (MRI) be dangerous for patients?; What is the difference between MRI and fMRI?; Is ultrasound (US) suited for which type of tissue? When are PET and SPECT scanners used? What is an angiography system?; How can medical imaging support the detection and treatment of cancer?; What is Hybrid Imaging? Why do we need so many different imaging modalities?
Goals
In this seminar, you will learn about commonly used imaging modalities (e.g., MRI, CT, X-ray, US, …) used in healthcare by understanding their underlying physics, functionalities, and image acquisition processes.
You will further explore publicly available medical datasets of various anatomical regions while analyzing their different structures and formats.
In the end, you will be able to use your acquired knowledge to explore the potential as well as challenges of using medical data for current research. This seminar can be an essential building block if you are interested in building AI systems for healthcare applications.
Format
The presentations for this seminar will be conducted as block seminar. Dates TBD.
We will meet in the beginning of the semester to discuss work areas and assign concrete topics to each participant. You will be provided pointers to literature and then independently familiarize yourself with the assigned topic. Towards the end of the semester you will:
- present your topic as a 30 minute presentation and
- submit a written report of approximately 8 pages.
The presentations will be conducted as a block seminar towards the end of the semester. The weekly hours mentioned in the module description are an optional time slot to get support, guidance and feedback on your topic (as required).
Expected workload & Grading
The time (work load) of this module is expected to be roughly as follows:
- Attendance of seminar / presentation: 20h
- Literature review and familiarization with topic: 25h
- Preparation of presentation: 15h
- Written report: 30h
The grade will be determined in equal parts based on the presentation and report. Attendance of the presentations is mandatory.
- Englischsprachige Informationen:
- Title:
- xAI-Sem-B1: Bachelorseminar Erklärbares Maschinelles Lernen
- Credits: 3
- Institution: Lehrstuhl für Erklärbares Maschinelles Lernen
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