Titlebook: Machine Learning in Medical Imaging; 10th International W Heung-Il Suk,Mingxia Liu,Chunfeng Lian Conference proceedings 2019 Springer Natur

memoir

書目名稱Machine Learning in Medical Imaging影響因子(影響力)




書目名稱Machine Learning in Medical Imaging影響因子(影響力)學(xué)科排名




書目名稱Machine Learning in Medical Imaging網(wǎng)絡(luò)公開度




書目名稱Machine Learning in Medical Imaging網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning in Medical Imaging被引頻次




書目名稱Machine Learning in Medical Imaging被引頻次學(xué)科排名




書目名稱Machine Learning in Medical Imaging年度引用




書目名稱Machine Learning in Medical Imaging年度引用學(xué)科排名




書目名稱Machine Learning in Medical Imaging讀者反饋




書目名稱Machine Learning in Medical Imaging讀者反饋學(xué)科排名

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Conference proceedings 2019ICCAI 2019, in Shenzhen, China, in October 2019.?.The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions.?.They focus on major trends and challenges in the area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt w

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Conference proceedings 2019ng, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.?.

Ardent

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Misnomer

WSI-Net: Branch-Based and Hierarchy-Aware Network for Segmentation and Classification of Breast Histhe pathology hierarchical relationships between pixels in each patch. By aggregating patch segmentation results from WSI-Net, we generate a segmentation map for the WSI and extract its morphological features for WSI-level classification. Experimental results show that our WSI-Net can be ., . and . on our benchmark dataset.

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信條

MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network,ial multi-modal input images, and (3) a densely-dilated U-Net as the encoder-decoder backbone for image segmentation. Experiments on ISLES 2018 data set have shown that MSAFusionNet achieves the state-of-the-art segmentation accuracy.

CURT

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