167 citations · 220 across the 5 of their papers we have counts for
5 papers
OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
Maaike Galama, Nina Kozar-Gillan, Christina Embacher +18
The tumor microenvironment (TME) plays a central role in cancer progression, treatment response, and patient outcomes, yet large-scale, consistent, and quantitative TME characteriz…
Large deviations for a class of multivariate heavy-tailed risk processes used in insurance and finance
Miriam Hägele, Jaakko Lehtomaa
Modern risk modelling approaches deal with vectors of multiple components. The components could be, for example, returns of financial instruments or losses within an insurance port…
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…
iNNvestigate neural networks!
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer +7
In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. The…
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…