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Titlebook: Deep Learning in Healthcare; Paradigms and Applic Yen-Wei Chen,Lakhmi C. Jain Book 2020 Springer Nature Switzerland AG 2020 Deep Learning.M

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https://doi.org/10.1007/978-3-658-28110-6chitecture, optimization algorithm, activation functions and the number of convolution filters. With the designed network, we used relatively less training data than other segmentation methods. The direct output of our network, with no further post-processing, resulted in the dice score of ~99 in tr
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Overcrowding in mature destinationrain it with the DASL strategy. Experimental results show that the proposed models trained with our DASL strategy perform much better than those trained without DASL using the same amount of annotated samples.
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Im Spannungsfeld von Quantität und Qualitätined feature representations by an activation visualization method, and by measuring the frequency response of trained neural networks, in both qualitative and quantitative ways, respectively. These results demonstrate that such successive transfer learning enables networks to grasp both structural
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https://doi.org/10.1007/978-3-8350-5571-1y, based on the classification results, we also perform the quantitative analysis of emphysema in 50 subjects by correlating the quantitative results (the area percentage of each class) with pulmonary functions. We show that centrilobular emphysema (CLE) and panlobular emphysema (PLE) have strong co
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