Maschenka Balkenhol

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This paper presents and evaluates a fully automatic method for detection of ductal carcinoma in situ (DCIS) in digitized hematoxylin and eosin (H&E) stained histopathological slides of breast tissue. The proposed method applies multi-scale superpixel classification to detect epithelial regions in whole-slide images (WSIs). Subsequently, spatial clustering(More)
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, ductal carcinoma in situ (DCIS), and invasive ductal(More)
Our aim was to investigate whether Breast Imaging Reporting and Data System-Ultrasound (BI-RADS-US) lexicon descriptors can be used as imaging biomarkers to differentiate molecular subtypes (MS) of invasive ductal carcinoma (IDC) in automated breast ultrasound (ABUS). We included 125 IDCs diagnosed between 2010 and 2014 and imaged with ABUS at two(More)
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