Reading Course 'Computational Pathology: Algorithms and Applications'

 

Lecturers: Tingying Peng and Carsten Marr
Tel.:  +49 89 3187-4217
E-mail
Date and Time:  
  • Tuesdays, 16:00 - 17:30
  • via Zoom
  • Meeting ID: 665 1734 2361 Passcode: 694340
 
Target Group: Mathematicians, biologists, (bio-)statisticians, biotechnologists, bioinformaticians or equivalent with an interest in computational biology    
ECTS: 3 (without a final report) or 5 (with a final report)
Number of participants: ~20
Language:        English
Registration: via email to 
Material:

The following two papers give a good introduction into the topic:

  • van der Laak, J., Litjens, G., and Ciompi, F. (2021). Deep learning in histopathology: the path to the clinic. Nat. Med. 27, 775–784. Fuchs, T.J., and Buhmann, J.M. (2011).
  • Computational pathology: challenges and promises for tissue analysis. Comput. Med. Imaging Graph. 35, 515–530.
Topic:   Computational Pathology encompasses algorithms and methods that answer​ scientific and clinical questions in pathology. In the few last years,​ traditional analyses are challenged with deep learning methods that​ allow for more standardised, robust and powerful applications. In this​ seminar, we will study recent research papers that develop or apply​ deep learning methods in a pathology context. Whenever possible, we​ will re-implement the applied methods, analyse the used technologies​ and discuss the biomedical and clinical implications. After succesful completion of the module, the students will be able​ to read and evaluate scientific literature on computational pathology,​ and they have learned how to extract computational content and​ re-implement parts of the analysis. Finally, the course will​ strengthen the presentation and discussion skills of the participants.