Seminar Digital Pathology and Deep Learning (SemDP)5 ECTS
(englische Bezeichnung: Seminar Digital Pathology and Deep Learning)
(Prüfungsordnungsmodul: Seminar: Digital Pathology and Deep Learning)

Lehrveranstaltungen:

• • Seminar Digital Pathology and Deep Learning
    (Seminar, 2 SWS, Katharina Breininger et al., Di, 16:30 - 18:00, 02.133-113)

Empfohlene Voraussetzungen:

Students are required to have initial experience with deep learning and machine learning, e.g., from the module "Deep Learning".
This seminar is recommended for Master's students.

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

Lernziele und Kompetenzen:

Students will be able to

• perform their own literature research on a given subject
  

• independently research this subject
  

• present and introduce the subject to their student peers
  

• give a scientific talk in English according to international conference standards

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

1. Computational Engineering (Rechnergestütztes Ingenieurwesen) (Master of Science)
   (Po-Vers. 2013 | TechFak | Computational Engineering (Rechnergestütztes Ingenieurwesen) (Master of Science) | Gesamtkonto | Seminar, Masterarbeit | Seminar im Masterstudium | Seminar: Digital Pathology and Deep Learning)

Studien-/Prüfungsleistungen:

Seminar: Digital Pathology and Deep Learning (Prüfungsnummer: 76581)

(englischer Titel: Seminar Digital Pathology and Deep Learning)

Prüfungsleistung, Seminarleistung, Dauer (in Minuten): 30, benotet, 5 ECTS

Anteil an der Berechnung der Modulnote: 100.0 %

weitere Erläuterungen:
Die Gesamtnote setzt sich zu 50% aus der Bewertung des Vortrags und zu 50% aus der Bewertung der Ausarbeitung / Implementierung zusammen. Ziel des Seminars ist die verständliche Aufbereitung eines Themas für andere Studierende. Die Vortragsdauer beträgt 30 Minuten. Ziel ist es, diese möglichst genau einzuhalten. Die Ausarbeitung umfasst 6 Seiten im Stil von IEEE-Konferenzbeiträgen. Vortrag und Ausarbeitung sollten auf Englisch erfolgen. The overall grade consists of two parts: The evaluation of a 30 minute talk (50%) and the evaluation of a seminar paper / implementation (50%). The goal of the seminar is to prepare a topic for other students in an accessible way. The goal is to keep this time as closely as possible. The seminar paper comprises 6 pages in the style of IEEE conference contributions. Talk and seminar paper should be in English.

Prüfungssprache: Englisch

Erstablegung: SS 2021, 1. Wdh.: WS 2021/2022

1. Prüfer:

Andreas Maier