Machine-Learning based microscopy image analysis
Modern microscopy modalities that are used in the life sciences produce increasingly large image datasets which are difficult to interpret without the help of computational methods. In my talk, I will highlight several Machine Learning based approaches that we developed to address common image analysis problems in microscopy such as image restoration, nuclei/cell segmentation, and organelle reconstruction. I will demonstrate our methods on diverse examples ranging from 2D fluorescence microscopy, digital histopathology, light-sheet microscopy, to 3D electron microscopy. Finally, I will give an introduction to different software frameworks that we developed to make these methods available to the life science research community.
Dr. Martin Weigert
EPFL in Lausanne, Switzerland
Martin Weigert is a computer scientist whose research focuses on Machine Learning based image reconstruction and segmentation methods for microscopy. After doing his PhD at the MPI-CBG in Dresden, Germany he is currently a group leader at EPFL in Lausanne, Switzerland.