139 citations · 139 across the 2 of their papers we have counts for
2 papers
cs.LG2019
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data
Christian Etmann, Maximilian Schmidt, Jens Behrmann +6
Neural networks have recently been established as a viable classification method for imaging mass spectrometry data for tumor typing. For multi-laboratory scenarios however, certai…
stat.ML2017★ 139 cited
Deep Learning for Tumor Classification in Imaging Mass Spectrometry
Jens Behrmann, Christian Etmann, Tobias Boskamp +3
Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, a…