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Instance segmentation in electron microscopy (EM) volumes is tough due to complex shapes and sparse annotations.
“Machine learning of hierarchical clustering to segment 2d and 3d images,”
Juan Nunez-Iglesias, Ryan Kennedy, Toufiq Parag, Jianbo Shi, and Dmitri B Chklovskii, · 2013
Earlier work this paper cites.
“Saturated reconstruction of a volume of neocortex,”
Narayanan Kasthuri, Kenneth Jeffrey Hayworth, Daniel Raimund Berger, Richard Lee Schalek, José Angel Conchello, Seymour Knowles-Barley, Dongil Lee, Amelio Vázquez-Reina, Verena Kaynig, Thouis Raymond Jones, et al., · 2015
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“Crowdsourcing the creation of image segmentation algorithms for connectomics,”
Ignacio Arganda-Carreras, Srinivas C Turaga, and et al., · 2015
Earlier work this paper cites.
“Miccai challenge on circuit reconstruction from electron microscopy images,” 2016
J Funke, S Saalfeld, DD Bock, SC Turaga, and E Perlman, · 2016
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“A connectome of a learning and memory center in the adult drosophila brain,”
Shin-ya Takemura, Yoshinori Aso, Toshihide Hige, Allan Wong, Zhiyuan Lu, C Shan Xu, Patricia K Rivlin, Harald Hess, Ting Zhao, Toufiq Parag, et al., · 2017
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“Large scale image segmentation with structured loss based deep learning for connectome reconstruction,”
Jan Funke, Fabian Tschopp, William Grisaitis, and et al., · 2018
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“Momentum contrast for unsupervised visual representation learning,”
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick, · 2020
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“An image is worth 16x16 words: Transformers for image recognition at scale,”
Alexey Dosovitskiy, Lucas Beyer, and et al., · 2020
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“Mitoem dataset: Large-scale 3d mitochondria instance segmentation from em images,”
Donglai Wei, Zudi Lin, Daniel Franco-Barranco, Nils Wendt, Xingyu Liu, and et al., · 2020
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“Bootstrap your own latent-a new approach to self-supervised learning,”
Jean-Bastien Grill, Florian Strub, Florent Altché, and et al., · 2020
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“Barlow twins: Self-supervised learning via redundancy reduction,”
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny, · 2021
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“Automatic detection of synaptic partners in a whole-brain drosophila em dataset,”
Philipp Schlegel, Alexander S Bates, Tejal Parag, Gregory SXE Jefferis, and Davi D Bock, · 2021
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“Exploring simple siamese representation learning,”
Xinlei Chen and Kaiming He, · 2021
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“Local shape descriptors for neuron segmentation,”
Arlo Sheridan, Tri M Nguyen, Diptodip Deb, Wei-Chung Allen Lee, Stephan Saalfeld, Srinivas C Turaga, Uri Manor, and Jan Funke, · 2022
Cited alongside, same era.
“Advanced deep networks for 3d mitochondria instance segmentation,”
“Learning to model pixel-embedded affinity for homogeneous instance segmentation,”
Wei Huang, Shiyu Deng, Chang Chen, Xueyang Fu, and Zhiwei Xiong, · 2022
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“Unetr: Transformers for 3d medical image segmentation,”
Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, and et al., · 2022
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“Med-unic: Unifying cross-lingual medical vision-language pre-training by diminishing bias,”
Zhongwei Wan, Che Liu, Mi Zhang, Jie Fu, Benyou Wang, Sibo Cheng, Lei Ma, César Quilodrán-Casas, and Rossella Arcucci, · 2023
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Che Liu, Sibo Cheng, Chen Chen, Mengyun Qiao, Weitong Zhang, Anand Shah, Wenjia Bai, and Rossella Arcucci, · 2023
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“Self-supervised neuron segmentation with multi-agent reinforcement learning,”
Yinda Chen, Wei Huang, Shenglong Zhou, Qi Chen, and Zhiwei Xiong, · 2023
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Mingxing Li, Chang Chen, Xiaoyu Liu, Wei Huang, Yueyi Zhang, and Zhiwei Xiong, · 2022
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“Semi-supervised neuron segmentation via reinforced consistency learning,”
Wei Huang, Chang Chen, Zhiwei Xiong, Yueyi Zhang, Xuejin Chen, Xiaoyan Sun, and Feng Wu, · 2022
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“Self pre-training with masked autoencoders for medical image analysis,”
Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, Dimitris Samaras, and Prateek Prasanna, · 2022
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“Multi-granularity cross-modal alignment for generalized medical visual representation learning,”
Fuying Wang, Yuyin Zhou, Shujun Wang, and et al., · 2022
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“A unified visual information preservation framework for self-supervised pre-training in medical image analysis,”
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“Multi-level contrastive learning for self-supervised vision transformers,”
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“Generative text-guided 3d vision-language pretraining for unified medical image segmentation,”
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