Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow

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書(shū)目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影響因子(影響力)




書(shū)目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影響因子(影響力)學(xué)科排名




書(shū)目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引頻次




書(shū)目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引頻次學(xué)科排名




書(shū)目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用




書(shū)目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用學(xué)科排名




書(shū)目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020讀者反饋




書(shū)目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020讀者反饋學(xué)科排名

根除

978-3-030-59721-4Springer Nature Switzerland AG 2020

以煙熏消毒

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020978-3-030-59722-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

憤慨一下

https://doi.org/10.1007/978-3-030-59722-1artificial intelligence; color image processing; computer vision; deep learning; digital image; image ana

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verdict

DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imagis well as non-active cells. We first learn to predict whether two pixels belong to the same cell; this information is summarized in an undirected, edge-weighted graph which we then partition. Evaluating our method on the Neurofinder public benchmark shows that DISCo outperforms all existing models trained on these datasets.

VAN

Dealing

MitoEM Dataset: Large-Scale 3D Mitochondria Instance Segmentation from EM Imagesric for 3D data with a 45. speedup. On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances. Thus, our MitoEM dataset poses new challenges to the field. We release our code and data: ..

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