3 papers
eess.IV2020
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…
eess.IV2020
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…
eess.IV2020
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…