Enforcing Prediction Consistency Across Orthogonal Planes Significantly Improves Segmentation of FIB-SEM Image Volumes by 2D Neural Networks.
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Abstract
Ryan Conrad, Hanbin Lee, Kedar Narayan; Enforcing Prediction Consistency Across Orthogonal Planes Significantly Improves Segmentation of FIB-SEM Image Volu
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Source: openalex_inverted_index · Quality: short
Research context
OpenAlex topics: Electron and X-Ray Spectroscopy Techniques · Industrial Vision Systems and Defect Detection · Advanced Neural Network Applications
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References (6)
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1. 10.1007/978-3-030-01234-2_4910.1007/978-3-030-01234-2_49 ↗
2. 10.1109/3DV.2016.7910.1109/3DV.2016.79 ↗
3. [5] Smith, L. N. (2018). A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay. http://arxiv.org/abs/1803.09820
4. We thank Adam Harned for acquiring the FIB-SEM datasets used throughout this work and Dr. Stanley Lipkowitz for providing the cell samples. This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. 75N91019D00024. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.
5. 10.3389/fnana.2018.0009210.3389/fnana.2018.00092 ↗
6. [2] Reed, S. E. , Lee, H. , Anguelov, D. , Szegedy, C. , Erhan, D. , & Rabinovich, A. (2014). Training Deep Learning Neural Networks on Noisy Labels with Bootstrapping. https://arxiv.org/abs/1412.6596