activity
20182021
most citedBeyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.DC20211 cited

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Martin Uray, Eduard Hirsch, Gerold Katzinger +1

Enterprises and labs performing computationally expensive data science applications sooner or later face the problem of scale but unconnected infrastructure. For this up-scaling pr…

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

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…

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…

cs.CV2018

Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations

Michael Gadermayr, Laxmi Gupta, Barbara M. Klinkhammer +2

Recently, generative adversarial networks exhibited excellent performances in semi-supervised image analysis scenarios. In this paper, we go even further by proposing a fully unsup…