Home.md

@@ -37,6 +37,12 @@ HAICU Central will be implemented at HMGU, Munich. Other HAICU Local units will
 
 In this talk, we take a path through several different approaches to explainability in machine learning. First, we talk about categories of explainability, then we discuss approaches to relevance ranking in terms of engineered features and in terms of heat maps in images through deep Taylor expansion. We then provide a use case of a recent publication on using machine learning with MEG data, and suggest that explainability in brain data has room for improvement. Time permitting, we will briefly cover how explainable AI may help to overcome regulatory and cultural issues in healthcare and therefore accelerate the use of AI methods in practice.
 
+### Talk by [Mateusz Kozinski ](https://cvlab.epfl.ch/): "Learning to segment 3D linear structures with 2D annotations"
+* When: 18th July 2019, at 1:30 pm
+* Where: INM seminar room, building 15.9, room 4001b
+
+We propose a loss function for training a Deep Neural Network (DNN) to segment volumetric data, that accommodates ground truth annotations of 2D projections of the training volumes, instead of annotations of the 3D volumes themselves. In consequence, we significantly decrease the amount of annotations needed for a given training set. We apply the proposed loss to train DNNs for segmentation of vascular and neural networks in microscopy images and demonstrate only a marginal accuracy loss associated to the significant reduction of the annotation effort. The lower labor cost of deploying DNNs, brought in by our method, can contribute to a wide adoption of these techniques for analysis of 3D images of linear structures.
+
 [Archive Events](Archive Events) 
 
 # How to get in contact