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Architecture Optimization and Interpretability in Neural Networks for HRTEM Segmentation

Published online by Cambridge University Press:  30 July 2020

Catherine Groschner
Affiliation:
University of California Berkeley, Berkeley, California, United States
Mary Scott
Affiliation:
Lawrence Berkeley National Laboratory, Berkeley, California, United States

Abstract

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Type
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Copyright
Copyright © Microscopy Society of America 2020

References

Horwath, J. P. et al. Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation. ArXiv (2019).Google Scholar
Ronneberger, O., Fischer, P. & Brox, T. Lecture Notes in Computer Science (2015) p. 234241.10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
Madsen, J. et al. Adv. Theory Simul. 1800037 (2018) p. 112Google Scholar
Ziatdinov, M. et al. ACS Nano 11 (2017) p. 1274212752.10.1021/acsnano.7b07504CrossRefGoogle Scholar
Karim, M. R. et al. Proc. - 2019 IEEE 19th Int. Conf. Bioinforma. Bioeng. BIBE 2019 (2019). p. 415422Google Scholar
Drozdzal, M. et al. Carneiro G. et al. ( eds) Deep Learning and Data Labeling for Medical Applications. DLMIA 2016, LABELS 2016. Lecture Notes in Computer Science, 10008. (2016).10.1007/978-3-319-46976-8CrossRefGoogle Scholar
Shelhamer, E., Long, J. & Darrell, T. Fully Convolutional Networks for Semantic Segmentation. ArXiv (2016).Google ScholarPubMed
Simonyan, K. & Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. ArXiv (2014).Google Scholar
Chollet, Francois. Deep Learning with Python. Manning Publications, Shelter Island. (2017).Google Scholar
Gonzalez-Garcia, A., Modolo, D. & Ferrari, V. Int. J. Comput. Vis. 126 , 476494 (2018).10.1007/s11263-017-1048-0CrossRefGoogle Scholar
Montavon, G., Samek, W. & Müller, K. R. Digital Signal Processing 73 , 115 (2018).10.1016/j.dsp.2017.10.011CrossRefGoogle Scholar
Qin, Z., Yu, F., Liu, C. & Chen, X. How convolutional neural network see the world - A survey of convolutional neural network visualization methods. ArXiv (2018).Google Scholar