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Seminar Digital Pathology and Deep Learning

Lecturers

Details

Time and place:

This course will be held online until further notice. Please register via in StudOn starting from March 15, 2021. Note that equal chances for all applicants apply until March 27, midnight.

  • Tue 16:30-18:00, Room 02.133-113

Fields of study

  • WPF MT-MA from SEM 1
  • WPF MT-MA-BDV from SEM 1
  • WPF INF-MA from SEM 1

Content

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

Additional information

Expected participants: 15

www: https://www.studon.fau.de/crs3683634_join.html