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Seminar Digital Pathology and Deep Learning (SemDP)- Lecturers
- Prof. Dr.-Ing. habil. Andreas Maier, Dr.-Ing. Marc Aubreville, Prof. Dr.-Ing. Joachim Hornegger, PD Dr. Samir Jabari, Prof. Dr. med. Ingmar Blümcke
- Details
- Seminar
2 cred.h, ECTS studies, ECTS credits: 5
nur Fachstudium, Sprache Englisch, This course will be held online until the coronavirus pandemic is contained to such an extent that the Bavarian state government can allow face-to-face teaching again
Time and place: Mon 8:15 - 9:45, KH 1.021
- Fields of study
- WPF INF-MA ab 1
WPF MT-MA-BDV ab 1
WPF CE-MA-TA-MT ab 1
- Prerequisites / Organisational information
- Registration via StudOn:
https://www.studon.fau.de/crs2884557.html
- Contents
- 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 is 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 information:
- Credits: 5
- Additional information
- Keywords: algorithms; medical image processing
Expected participants: 10, Maximale Teilnehmerzahl: 16
Registration is required for this lecture.
Die Registration via: StudOn
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2020:
- Seminar Advanced Algorithms in Medical Image Processing (SemAAMIP)
- Seminar Digital Pathology and Deep Learning (SemDP)
- Department: Chair of Computer Science 5 (Pattern Recognition)
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