activity
20172019
most citedThe Random Forest Classifier in WEKA: Discussion and New Developments for Imbalanced Data

15 citations · 29 across the 3 of their papers we have counts for

collaborators

5 papers

cs.HC2019

A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems

Mario Amrehn, Stefan Steidl, Reinier Kortekaas +4

For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small am…

cs.CV201915 cited

The Random Forest Classifier in WEKA: Discussion and New Developments for Imbalanced Data

Mario Amrehn, Firas Mualla, Elli Angelopoulou +2

Data analysis and machine learning have become an integrative part of the modern scientific methodology, providing automated techniques to predict further information based on obse…

cs.CV2018

Action Learning for 3D Point Cloud Based Organ Segmentation

Xia Zhong, Mario Amrehn, Nishant Ravikumar +6

We propose a novel point cloud based 3D organ segmentation pipeline utilizing deep Q-learning. In order to preserve shape properties, the learning process is guided using a statist…

cs.CV20172 cited

Robust Seed Mask Generation for Interactive Image Segmentation

Mario Amrehn, Stefan Steidl, Markus Kowarschik +1

In interactive medical image segmentation, anatomical structures are extracted from reconstructed volumetric images. The first iterations of user interaction traditionally consist…

cs.CV201712 cited

UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model

Mario Amrehn, Sven Gaube, Mathias Unberath +6

For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data…