101 citations · 204 across the 16 of their papers we have counts for
8 papers · 1 filter
Multimodal Learning With Intraoperative CBCT & Variably Aligned Preoperative CT Data To Improve Segmentation
Maximilian E. Tschuchnig, Philipp Steininger, Michael Gadermayr
Cone-beam computed tomography (CBCT) is an important tool facilitating computer aided interventions, despite often suffering from artifacts that pose challenges for accurate interp…
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction
Maximilian Ernst Tschuchnig, Julia Coste-Marin, Philipp Steininger +1
Semantic segmentation is a crucial task in medical image processing, essential for segmenting organs or lesions such as tumors. In this study we aim to improve automated segmentati…
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…
Anomaly Detection in Medical Imaging -- A Mini Review
Maximilian E. Tschuchnig, Michael Gadermayr
The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts…
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…
Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential
Maximilian Ernst Tschuchnig, Gertie Janneke Oostingh, Michael Gadermayr
Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole slide scanners enables the fast acquisition of large amount…
Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities
Georg Wimmer, Michael Gadermayr, Andreas Vécsei +1
Novel imaging technologies raise many questions concerning the adaptation of computer-aided decision support systems. Classification models either need to be adapted or even newly…
An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans
Michael Gadermayr, Maximilian Tschuchnig, Laxmi Gupta +4
Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medica…