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|a 10.1109/TPAMI.2024.3409634
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|a eng
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| 100 |
1 |
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|a Li, Zhenchen
|e verfasserin
|4 aut
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| 245 |
1 |
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|a DeepMulticut
|b Deep Learning of Multicut Problem for Neuron Segmentation From Electron Microscopy Volume
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a Date Revised 08.11.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Superpixel aggregation is a powerful tool for automated neuron segmentation from electron microscopy (EM) volume. However, existing graph partitioning methods for superpixel aggregation still involve two separate stages-model estimation and model solving, and therefore model error is inherent. To address this issue, we integrate the two stages and propose an end-to-end aggregation framework based on deep learning of the minimum cost multicut problem called DeepMulticut. The core challenge lies in differentiating the NP-hard multicut problem, whose constraint number is exponential in the problem size. With this in mind, we resort to relaxing the combinatorial solver-the greedy additive edge contraction (GAEC)-to a continuous Soft-GAEC algorithm, whose limit is shown to be the vanilla GAEC. Such relaxation thus allows the DeepMulticut to integrate edge cost estimators, Edge-CNNs, into a differentiable multicut optimization system and allows a decision-oriented loss to feed decision quality back to the Edge-CNNs for adaptive discriminative feature learning. Hence, the model estimators, Edge-CNNs, can be trained to improve partitioning decisions directly while beyond the NP-hardness. Also, we explain the rationale behind the DeepMulticut framework from the perspective of bi-level optimization. Extensive experiments on three public EM datasets demonstrate the effectiveness of the proposed DeepMulticut
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|a Journal Article
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1 |
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|a Yang, Xu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Jiazheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hong, Bei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Yanchao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhai, Hao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shen, Lijun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Xi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Zhiyong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Han, Hua
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 46(2024), 12 vom: 04. Nov., Seite 8696-8714
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:46
|g year:2024
|g number:12
|g day:04
|g month:11
|g pages:8696-8714
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|u http://dx.doi.org/10.1109/TPAMI.2024.3409634
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