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MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis. (arXiv:2211.05862v1 [cs.CV])
Nov. 14, 2022, 2:14 a.m. | Michael Gadermayr, Lukas Koller, Maximilian Tschuchnig, Lea Maria Stangassinger, Christina Kreutzer, Sebastien Couillard-Despres, Gertie Janneke Oosti
cs.CV updates on arXiv.org arxiv.org
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, leading to a small number of labeled samples. To improve
training, we propose and investigate different data augmentation strategies for
multiple instance learning based on the idea of linear interpolations of
feature vectors (known as MixUp). Based on state-of-the-art multiple instance …
arxiv augmentation cancer cancer diagnosis data diagnosis mil study
More from arxiv.org / cs.CV updates on arXiv.org
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