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Vorlesungsverzeichnis >> Technische Fakultät (TF) >>
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Seminar Digital Pathology and Deep Learning (SemDP)- Dozentinnen/Dozenten
- Prof. Dr.-Ing. Katharina Breininger, Prof. Dr.-Ing. habil. Andreas Maier, Prof. Dr. med. Samir Jabari, Prof. Dr. med. Ingmar Blümcke, Christian Marzahl, M. Sc.
- Angaben
- Seminar
Online/Präsenz
2 SWS, ECTS-Studium, ECTS-Credits: 5, Sprache Englisch, This course will be held in a hybrid format. The first session (Oct. 19) will be via Zoom, further details will be shared at the beginning of the seminar. Please register for this course via StudOn.
Zeit und Ort: Di 16:30 - 18:00, 02.133-113
- Studienfächer / Studienrichtungen
- WPF MT-MA 1 (ECTS-Credits: 5)
WPF MT-MA-BDV 1 (ECTS-Credits: 5)
WPF INF-MA 1 (ECTS-Credits: 5)
- Inhalt
- Pathology is the study of diseases and aims to deliver a fine-grained diagnosis to understand processes in the body as well as to enable targeted treatment. In this area, the opportunities for digital image processing are vast: While the need for precision medicine, i.e., taking into account various co-dependencies when formulating the best possible treatment for a patient, is high, the number of pathologists ist not increasing accordingly. Deep learning-based techniques can be used for different objectives in this scope. Examples include screening large microscopy images for specific rare events, providing visual augmentation with analysis data. Additionally, the availability of massive data collections, including genomics and further biological factors, can be utilized to determine specific information about diseases that were previously unavailable.
This seminar is offered to students of medicine as well as computer sciences and medical engineering and similar. Students will have to present a topic from this field in a short (30 min) and comprehensive presentation.List of topics:
Staining and special stains (including immunohistochemistry, enzyme-based dyes and tissue microarrays)
Current computational pathology
Knowledge/Feature fusion into a diagnosis
Histopathology quality control
Data sets as limiting factor - limits of current data sets
Large scale / clinical grade solutions
Computational and augmented tumor grading
In vivo microstructural analysis
Big data in pathology (multi-omics)
Histology image registration
Staining differences and stain normalization
Transfer learning and domain adaptation
Explainable AI
Virtual staining
Digital workflow in Germany vs. the world
Limits of digital pathology
- ECTS-Informationen:
- Credits: 5
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 15, Maximale Teilnehmerzahl: 15
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2021/2022:
- Biomedizin und Hauptseminar Medizintechnik (BuHSMT)
- Seminar Digital Pathology and Deep Learning (SemDP)
- Institution: Juniorprofessur für Artificial Intelligence in Medical Imaging
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