101 citations · 117 across the 16 of their papers we have counts for
4 papers · 1 filter
CBCTLiTS: A Synthetic, Paired CBCT/CT Dataset For Segmentation And Style Transfer
Maximilian E. Tschuchnig, Philipp Steininger, Michael Gadermayr
Medical imaging is vital in computer assisted intervention. Particularly cone beam computed tomography (CBCT) with defacto real time and mobility capabilities plays an important ro…
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…
Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential
Michael Gadermayr, Maximilian Tschuchnig
Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural…