A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy

Yan Zhang; Steve Farrell; Michael Crowley; Lee Makowski; Jack Deslippe

doi:10.1364/TRANSLATIONAL.2020.JTh2A.28

Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN)
OSA Technical Digest (Optica Publishing Group, 2020),
paper JTh2A.28
•https://doi.org/10.1364/TRANSLATIONAL.2020.JTh2A.28

A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy

Yan Zhang, Steve Farrell, Michael Crowley, Lee Makowski, and Jack Deslippe

Microscopy Histopathology and Analytics 2020

Washington, DC United States
20–23 April 2020
ISBN: 978-1-943580-74-3

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Joint Poster Session III (JTh2A)

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Y. Zhang, S. Farrell, M. Crowley, L. Makowski, and J. Deslippe, "A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy," in Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN), OSA Technical Digest (Optica Publishing Group, 2020), paper JTh2A.28.

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Abstract

An image dataset of 10 different size molecules, where each molecule has 2,000 structural variants, is generated from the 2D cross-sectional projection of Molecular Dynamics trajectories. The purpose of this dataset is to provide a benchmark dataset for the increasing need of machine learning, deep learning and image processing on the study of scattering, imaging and microscopy.

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