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Titlebook: Integer Programming and Combinatorial Optimization; 19th International C Friedrich Eisenbrand,Jochen Koenemann Conference proceedings 2017

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樓主: Fruition
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發(fā)表于 2025-3-25 06:37:51 | 只看該作者
Pierre Bonami,Andrea Lodi,Andrea Tramontani,Sven Wiesem, the aggravation of the wound often leads to limb amputation. Throughout this book, important concepts such as multidisciplinary approach, understanding the overall systemic condition, improving vasculopathy, treating infection, and wound bed preparation all leads to enhance the outcome for recons
22#
發(fā)表于 2025-3-25 07:47:28 | 只看該作者
Deeparnab Chakrabarty,Ravishankar Krishnaswamy,Amit Kumarrious diseases including foot ulcerations. The development of the disease is characterized under different phases ranging from controlled conditions toward the uncontrolled conditions leading toward the surgeries and mortality. The occurrence of the disease is most common among patients who are diab
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發(fā)表于 2025-3-25 12:54:27 | 只看該作者
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發(fā)表于 2025-3-25 19:05:12 | 只看該作者
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發(fā)表于 2025-3-25 21:20:01 | 只看該作者
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發(fā)表于 2025-3-26 03:30:13 | 只看該作者
Szymon Dudycz,Jan Marcinkowski,Katarzyna Paluch,Bartosz Rybickiop a self monitoring mobile application, in this work, we propose a novel deep subspace analysis pipeline for semi-supervised diabetic foot ulcer mulit-label classification. To avoid risk of over-fitting, the proposed pipeline dose not include any data augmentation. Whereas, after extracting deep fe
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發(fā)表于 2025-3-26 04:50:24 | 只看該作者
28#
發(fā)表于 2025-3-26 08:48:16 | 只看該作者
Zachary Friggstad,Chaitanya Swamy and ischemia in DFU can significantly prolong treatment and often result in limb amputation, with more severe cases resulting in terminal illness. Thus, early identification and regular monitoring is necessary to improve care, and reduce the burden on healthcare systems. With that in mind, this stu
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發(fā)表于 2025-3-26 13:02:45 | 只看該作者
30#
發(fā)表于 2025-3-26 18:18:20 | 只看該作者
Varun Gupta,Benjamin Moseley,Marc Uetz,Qiaomin Xie deep learning classification networks. The presence of binary-identical duplicate images in datasets used to train deep learning algorithms is a well known issue that can introduce unwanted bias which can degrade network performance. However, the effect of visually similar non-identical images is a
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