167 citations · 189 across the 4 of their papers we have counts for
4 papers
Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning
Marcel Bengs, Satish Pant, Michael Bockmayr +2
Medulloblastoma (MB) is a primary central nervous system tumor and the most common malignant brain cancer among children. Neuropathologists perform microscopic inspection of histop…
Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets
Marcel Bengs, Michael Bockmayr, Ulrich Schüller +1
Medulloblastoma (MB) is the most common malignant brain tumor in childhood. The diagnosis is generally based on the microscopic evaluation of histopathological tissue slides. Howev…
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
Miriam Hägele, Philipp Seegerer, Sebastian Lapuschkin +5
Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better unde…
Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles
Alexander Binder, Michael Bockmayr, Miriam Hägele +15
Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or…