Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning

no code implementations • 14 Sep 2021 • Marcel Bengs, Satish Pant, Michael Bockmayr, Ulrich Schüller, Alexander Schlaefer

Our top-performing method achieves the AUC-ROC value of 90. 90\% compared to 84. 53\% using the previous approach with smaller input tiles.

Classification Transfer Learning

Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets

no code implementations • 10 Sep 2021 • Marcel Bengs, Michael Bockmayr, Ulrich Schüller, Alexander Schlaefer

In this work, we propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions.

Transfer Learning

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

no code implementations • 15 Aug 2019 • Miriam Hägele, Philipp Seegerer, Sebastian Lapuschkin, Michael Bockmayr, Wojciech Samek, Frederick Klauschen, Klaus-Robert Müller, Alexander Binder

Deep learning has recently gained popularity in digital pathology due to its high prediction quality.

Binary Classification

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

no code implementations • 28 May 2018 • Alexander Binder, Michael Bockmayr, Miriam Hägele, Stephan Wienert, Daniel Heim, Katharina Hellweg, Albrecht Stenzinger, Laura Parlow, Jan Budczies, Benjamin Goeppert, Denise Treue, Manato Kotani, Masaru Ishii, Manfred Dietel, Andreas Hocke, Carsten Denkert, Klaus-Robert Müller, Frederick Klauschen

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 molecular features, but usually not both.

BIG-bench Machine Learning