Speaker
Luca Curcuraci
(Max-Planck-Institut für Kolloid- und Grenzflächenforschung)
Description
Segmentation and analysis of structures in 3d biological samples may be an ambiguous operation, due to the difficulties in the data visualization. Machine learning may help for this kind of task, but they may remain opaque regarding the scientific reasons leading to a particular result. Thanks to recent advancement in the field of explainable machine learning, human interpretable explanations can be still obtained, suggesting possible investigation direction. In this talk a procedure for the automatic analysis of 3d texture-like properties in biological samples, the extraction of human interpretable explanation is briefly presented, together with practical applications to real data.
Primary authors
Andreas Marek
(MPCDF)
Luca Curcuraci
(Max-Planck-Institut für Kolloid- und Grenzflächenforschung)
Luca Bertinetti
Markus Kühbach
(Fritz-Haber-Institut der Max-Planck-Gesellschaft GmbH)
Markus Rampp
(Max Planck Computing and Data Facility (MPCDF))
Nicolas Fabas
Peter Fratzl
(Max Planck Institute of Colloids and Interfaces)
Richard Weinkamer
(Max Planck Institute of Colloids and Interfaces, Department of Biomaterials)
Ronald Seidel
