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pro vyhledávání: '"Orgun, Mehmet A."'
Machine learning models for medical images can help physicians diagnose and manage diseases. However, due to the fact that medical image annotation requires a great deal of manpower and expertise, as well as the fact that clinical departments perform
Externí odkaz:
http://arxiv.org/abs/2311.10251
Autor:
Pang, Shuchao, Du, Anan, Orgun, Mehmet A., Wang, Yan, Sheng, Quan Z., Wang, Shoujin, Huang, Xiaoshui, Yu, Zhenmei
Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance, many challeng
Externí odkaz:
http://arxiv.org/abs/2207.14472
Autor:
Wang, Shoujin, Hu, Liang, Wang, Yan, He, Xiangnan, Sheng, Quan Z., Orgun, Mehmet A., Cao, Longbing, Ricci, Francesco, Yu, Philip S.
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics for
Externí odkaz:
http://arxiv.org/abs/2105.06339
Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, fro
Externí odkaz:
http://arxiv.org/abs/2009.06215
Autor:
Pang, Shuchao, Du, Anan, Orgun, Mehmet A., Wang, Yan, Sheng, Quanzheng, Wang, Shoujin, Huang, Xiaoshui, Yu, Zhemei
Automatic tumor segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on convolutional neural networks (CNNs) have achieved the state-of-the-art performance, many challenges still r
Externí odkaz:
http://arxiv.org/abs/2005.03924
Autor:
Wang, Shoujin, Hu, Liang, Wang, Yan, He, Xiangnan, Sheng, Quan Z., Orgun, Mehmet, Cao, Longbing, Wang, Nan, Ricci, Francesco, Yu, Philip S.
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ the advanced graph learning approaches to model users' preferences and intentions as well as items' characte
Externí odkaz:
http://arxiv.org/abs/2004.11718
Publikováno v:
Twenty-Third Pacific Asia Conference on Information Systems, China 2019
Existing Recommender Systems mainly focus on exploiting users' feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data S
Externí odkaz:
http://arxiv.org/abs/2001.04825
The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and mode
Externí odkaz:
http://arxiv.org/abs/2001.04830
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