Michael Gadermayr | DeepAI

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Dorit Merhof
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∙ 11/10/2022

MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis

Multiple instance learning exhibits a powerful approach for whole slide ...

0 Michael Gadermayr, et al. ∙

∙ 06/09/2022

Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations Future Potential

Digital whole slides images contain an enormous amount of information pr...

0 Michael Gadermayr, et al. ∙

∙ 04/22/2022

Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification

Thyroid cancer is currently the fifth most common malignancy diagnosed i...

0 Maximilian E. Tschuchnig, et al. ∙

∙ 10/11/2021

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Enterprises and labs performing computationally expensive data science a...

17 Martin Uray, et al. ∙

∙ 08/25/2021

Anomaly Detection in Medical Imaging – A Mini Review

The increasing digitization of medical imaging enables machine learning ...

0 Maximilian E. Tschuchnig, et al. ∙

∙ 12/15/2020

Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks

In contrast to paraffin sections, frozen sections can be quickly generat...

0 Michael Gadermayr, et al. ∙

∙ 04/30/2020

Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential

Image analysis in the field of digital pathology has recently gained inc...

0 Maximilian Ernst Tschuchnig, et al. ∙

∙ 04/27/2020

Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities

Novel imaging technologies raise many questions concerning the adaptatio...

0 Georg Wimmer, et al. ∙

∙ 04/23/2020

An Asymetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans

Generative adversarial networks using a cycle-consistency loss facilitat...

0 Michael Gadermayr, et al. ∙

∙ 05/25/2018

Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations

Recently, generative adversarial networks exhibited excellent performanc...

0 Michael Gadermayr, et al. ∙

∙ 08/01/2017

CNN Cascades for Segmenting Whole Slide Images of the Kidney

Due to the increasing availability of whole slide scanners facilitating ...

0 Michael Gadermayr, et al. ∙

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