6 citations · 6 across the 4 of their papers we have counts for
4 papers
MixUp-MIL: A Study on Linear & Multilinear Interpolation-Based Data Augmentation for Whole Slide Image Classification
Michael Gadermayr, Lukas Koller, Maximilian Tschuchnig +5
For classifying digital whole slide images in the absence of pixel level annotation, typically multiple instance learning methods are applied. Due to the generic applicability, suc…
MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis
Michael Gadermayr, Lukas Koller, Maximilian Tschuchnig +5
Multiple instance learning exhibits a powerful approach for whole slide image-based diagnosis in the absence of pixel- or patch-level annotations. In spite of the huge size of hole…
Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification
Maximilian E. Tschuchnig, Philipp Grubmüller, Lea M. Stangassinger +5
Thyroid cancer is currently the fifth most common malignancy diagnosed in women. Since differentiation of cancer sub-types is important for treatment and current, manual methods ar…
Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks
Michael Gadermayr, Maximilian Tschuchnig, Lea Maria Stangassinger +4
In contrast to paraffin sections, frozen sections can be quickly generated during surgical interventions. This procedure allows surgeons to wait for histological findings during th…