This is a block seminar covering topics related biomedical image processing and analysis.
Tutor: Dr. Pedro Guimarães

Registration *from October 21 to 27, 2019 HERE
Kick-off meeting [optional]October 29, 2019 (time 11:00) – E2.1, room 206
Deadline to (de-)register in HISPOS OR de-register from seminar *November 19, 2019 (3 weeks after the kick-off meeting)
Deadline for feedback ** [optional]January 24, 2020
PresentationsFebruary 28, 2020 (place/time: see below)
Summary submission deadline (1 week after the presentations)March 5, 2020
* If you want to de-register from the seminar, please send the tutor an email irrespectively whether you (de)registered in HISPOS or not.

**If you would like to get feedback about your slides, e.g. to improve your presentation before the talk, send your slides to the tutor before the feedback deadline. We strongly encourage you to take this opportunity. Before sending in the slides, check out our presentations guidelines. Please note: The more complete the submitted presentation the more helpful the feedback can be. Please try to avoid submitting half-finished slides.

Place and Time:

  • E2.1, room 206, 10:00

Certificate requirements:

  • Successful presentation:
    • Talk: 30 minutes (seminar)
    • Questions from the audience after the presentation
  • Attendance to all presentations
  • Submitting a summary (ca. 2 pages, excluding title (page), references, figures, tables etc.)


1SeminarARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning
2SeminarIntegrating spatial configuration into heatmap regression based CNNs for landmark localization
3SeminarA Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
4SeminarDAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
5SeminarClinically applicable deep learning framework for organs at risk delineation in CT images
6SeminarDevelopment of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy