Published Versions 1 Vol 2 (4) : 513–528 2020
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Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification
: 2019 - 09 - 06
: 2020 - 07 - 27
: 2020 - 07 - 30
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Abstract & Keywords
Abstract: In this paper, we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope (SEM). This is done by coupling supervised and unsupervised learning approaches. We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy. Then, we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scale (from 1 µm to 2 µm). Finally, we compare different clustering methods to uncover intrinsic structures in the images.
Keywords: Neural networks; Feature learning; Clustering analysis; Scanning Electron Microscope (SEM); Image classification
Acknowledgements
This work has been done within the NFFA-EUROPE project and has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 654360 NFFA-EUROPE. The authors thank A. Cazzaniga for his contribution in preparing the final version of the plots.
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Article and author information
Cite As
R. Aversa, P. Coronica, C. De Nobili & S. Cozzini. Deep learning, feature learning, and clustering analysis for SEM image classification. Data Intelligence 2(2020). doi: 10.1162/dint_a_00062
Rossella Aversa
R. Aversa wrote and revised the manuscript. She planned the overall work and performed the supervised approach.
Rossella Aversa got a PhD in Astrophysics at the International School for Advanced Studies (SISSA-ISAS) in Trieste, Italy. In the same town, she finalized her studies with a Master in High Performance Computing (MHPC) and worked as postdoc at CNR-IOM for three years, acquiring experience in machine learning techniques and data management. She is currently employed at Karlsruhe Institute of Technology (KIT) in Karlsruhe, Germany.
0000-0003-2534-0063
Piero Coronica
P. Coronica performed the unsupervised approach and reviewed the manuscript.
Piero Coronica holds a PhD in Geometry from the International School for Advanced Studies (SISSA-ISAS, Trieste) combined with further post graduate studies in High Performance Computing and data science. He currently works as part of the HPC team at the University of Cambridge’s Research Computing Services assisting national and international research groups to carry outacademic work. The projects he has been involved in apply state-of-the-art machine learning techniques to advance active research fields ranging from astronomy to digital humanities and medical imaging.
0000-0001-8235-1899
Cristiano De Nobili
C. De Nobili performed the supervised approach and reviewed the manuscript.
Cristiano De Nobili is a theoretical particle physicist. After his PhD in statistical physics at the International School for Advanced Studies (SISSAISAS) (2016) and a further master in High-Performance Computing (MHPC), he has been involved in deep learning. Starting from computer vision he is now a senior deep learning scientist in the field of Natural Language Processingworking on Samsung's virtual assistant. Moreover, Cristiano is a machine/deep learning instructor for several masters in both the private and academic sectors. He is an active speaker and recently gave a TEDx talk on ArtificialIntelligence.
0000-0002-8429-1831
Stefano Cozzini
S. Cozzini planned the overall work, supervised the whole work and reviewed the manuscript.
cozzini@areasciencepark.it
Stefano Cozzini is presently director of the Institute of Research and Technologies at Area Science Park where he coordinates several scientific infrastructures and projects at national and international level. He has more than 20 years’ experience in the area of scientific computing and HPC/Data e-infrastructures. His main scientific interests are scientific computing andmachine learning techniques applied to scientific data management. He is presently actively involved in the master’s degree on Data Science and Scientific Computing master at University of Trieste, Italy
0000-0001-6049-5242
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Published: Dec. 17, 2020 (Versions1
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