no code implementations • 29 Jan 2024 • Bastian Pfeifer, Christel Sirocchi, Marcus D. Bloice, Markus Kreuzthaler, Martin Urschler
In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data.
1 code implementation • 8 Nov 2023 • Hubert Baniecki, Maciej Chrabaszcz, Andreas Holzinger, Bastian Pfeifer, Anna Saranti, Przemyslaw Biecek
Evaluating explanations of image classifiers regarding ground truth, e. g. segmentation masks defined by human perception, primarily evaluates the quality of the models under consideration rather than the explanation methods themselves.
1 code implementation • 15 Jul 2023 • Bastian Pfeifer, Mateusz Krzyzinski, Hubert Baniecki, Anna Saranti, Andreas Holzinger, Przemyslaw Biecek
Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable.
1 code implementation • 6 Jun 2023 • Bastian Pfeifer
Random Forests are powerful ensemble learning algorithms widely used in various machine learning tasks.
1 code implementation • 30 Sep 2022 • Bastian Pfeifer, Marcus D. Bloice, Michael G. Schimek
We apply and validate our methodology on real-world multi-view cancer patient data.
no code implementations • 27 Dec 2021 • Markus Kreuzthaler, Bastian Pfeifer, Diether Kramer, Stefan Schulz
Clinical information systems have become large repositories for semi-structured and partly annotated electronic health record data, which have reached a critical mass that makes them interesting for supervised data-driven neural network approaches.
1 code implementation • 26 Aug 2021 • Bastian Pfeifer, Hubert Baniecki, Anna Saranti, Przemyslaw Biecek, Andreas Holzinger
To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well.