Titlebook: Innovationen und Innovationsmanagement im Gesundheitswesen; Technologien, Produk Mario A. Pfannstiel,Kristin Kassel,Christoph Rasch Book 20

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ysicians for accurate diagnosis. In this chapter, a completely integrated CAD system based on deep learning is presented to diagnose breast lesions from digital X-ray mammograms involving detection, segmentation, and classification. To automatically detect breast lesions from mammograms, a regional

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Heiko Block,Mareike Heinzen,Nils von Dellingshausenetal modeling based on computational anatomy, deep learning-based methods can obtain muscle information automatically. Through analysis of image features, both approaches can obtain muscle characteristics such as shape, volume, and area, and derive additional information by analyzing other image tex

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Ariane Segelitz-Karsten,Nadine Hietschold,Sebastian Gurtner,Ronny Reinhardt-dimensional (3D) computed tomography (CT) images. CT images are widely used to visualize 3D anatomical structures composed of multiple organ regions inside the human body in clinical medicine. Automatic recognition and segmentation of multiple organs on CT images is a fundamental processing step of

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Waldemar Pelzich is expensive to obtain. Active Learning (AL) frameworks can facilitate major improvements in CNN performance with intelligent selection of minimal data to be labeled. This paper proposes a novel diversified AL based on Fisher information (FI) for the first time for CNNs, where gradient computati