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Titlebook: Indolente Lymphome; Martin Dreyling,Marco Ladetto Book 2023 Der/die Herausgeber bzw. der/die Autor(en), exklusiv lizenziert an Springer Na

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發(fā)表于 2025-3-28 16:16:35 | 只看該作者
42#
發(fā)表于 2025-3-28 21:51:01 | 只看該作者
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發(fā)表于 2025-3-29 02:37:50 | 只看該作者
44#
發(fā)表于 2025-3-29 04:55:06 | 只看該作者
Lena Specht,Mario Levis,Umberto Ricardiesult in mis-segmentations. In the present paper we demonstrate that an affine transformation, and particularly, an elastic transformation yield an excellent patient motion compensation which is a sufficient basis for the segmentation algorithm. We describe a registration procedure based on the esti
45#
發(fā)表于 2025-3-29 11:01:05 | 只看該作者
ed that a post-processing layer exploiting the devised visual features is able a) to reduce the false alarm rate by about 10% to 20%, while keeping the number of true positives almost unaltered, and b) to generalize over different object classes and application domains.
46#
發(fā)表于 2025-3-29 13:15:56 | 只看該作者
47#
發(fā)表于 2025-3-29 16:01:01 | 只看該作者
Luca Arcaini,Andreas Viardotthe edge side. While our architectural approach is described as a design oriented issue, in this work we present our experience with the deep neural network (DNN) computational core, using a literature dataset from an air compression engine. We demonstrate that our approach is not only comparable wi
48#
發(fā)表于 2025-3-29 22:09:25 | 只看該作者
49#
發(fā)表于 2025-3-30 03:42:02 | 只看該作者
Christian Buske,Véronique Leblondzation, contour evidence extraction, and contour estimation. For the binarization, a model based on the U-Net architecture is trained to convert an input image into its binarized version. The contour evidence extraction starts by recovering contour segments from a binarized image using concave conto
50#
發(fā)表于 2025-3-30 07:54:17 | 只看該作者
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