Výsledky vyhledávání - "Oostingh, Gertie Janneke"

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

Autor: Gadermayr, Michael, Koller, Lukas, Tschuchnig, Maximilian, Stangassinger, Lea Maria, Kreutzer, Christina, Couillard-Despres, Sebastien, Oostingh, Gertie Janneke, Hittmair, Anton

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 slide images, the number of individual slides is often rather small,

Externí odkaz: http://arxiv.org/abs/2211.05862

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

Autor: Gadermayr, Michael, Tschuchnig, Maximilian, Stangassinger, Lea Maria, Kreutzer, Christina, Couillard-Despres, Sebastien, Oostingh, Gertie Janneke, Hittmair, Anton

In contrast to paraffin sections, frozen sections can be quickly generated during surgical interventions. This procedure allows surgeons to wait for histological findings during the intervention to base intra-operative decisions on the outcome of the

Externí odkaz: http://arxiv.org/abs/2012.08158

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

Autor: Tschuchnig, Maximilian Ernst, Oostingh, Gertie Janneke, Gadermayr, Michael

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 amounts of image data, showing extensive context and microscopic detail at t

Externí odkaz: http://arxiv.org/abs/2004.14936

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