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Yongluan Yan
Researcher at Huazhong University of Science and Technology
Publications - 6
Citations - 764
Yongluan Yan is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 5, co-authored 6 publications receiving 489 citations.
Papers
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Journal ArticleDOI
Revisiting multiple instance neural networks
TL;DR: This article revisit Multiple Instance Neural Networks (MINNs) that the neural networks aim at solving the MIL problems and proposes a new type of MINN to learn bag representations, which is different from the existing MINNs that focus on estimating instance label.
Book ChapterDOI
Weakly Supervised Region Proposal Network and Object Detection
Peng Tang,Xinggang Wang,Angtian Wang,Yongluan Yan,Wenyu Liu,Junzhou Huang,Junzhou Huang,Alan L. Yuille +7 more
TL;DR: This paper proposes a weakly supervised region proposal network which is trained using only image-level annotations and achieves the state-of-the-art performance for WSOD with performance gain of about \(3\%\) on average.
Journal ArticleDOI
Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning
Xinggang Wang,Yang Wei,Jeffrey C. Weinreb,Juan Han,Qiubai Li,Xiangchuang Kong,Yongluan Yan,Zan Ke,Bo Luo,Liu Tao,Liang Wang,Liang Wang +11 more
TL;DR: The results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients and the method is extensible to image modalities such as MR imaging, CT and PET of other organs.
Proceedings Article
Deep Multi-instance Learning with Dynamic Pooling
TL;DR: The proposed dynamic pooling based multi-instance neural network is an adaptive scheme for both key instance selection and modeling the contextual information among instances in a bag and can interpret instance-to-bag relationship.
Journal ArticleDOI
Bag similarity network for deep multi-instance learning
TL;DR: This study proposes a novel neural network for MIL that emphasizes modeling the affinities between bags, and achieves a more effective bag representation than previous methods.