Where is the Machine Looking? : Locating Discriminative Light-Scattering Features by Class-Activation Mapping

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Piedra , P , Gobert , C , Kalume , A , Pan , Y-L , Kocifaj , M , Muinonen , K , Penttilä , A , Zubko , E & Videen , G 2020 , ' Where is the Machine Looking? Locating Discriminative Light-Scattering Features by Class-Activation Mapping ' , Journal of Quantitative Spectroscopy & Radiative Transfer , vol. 247 , 106936 . https://doi.org/10.1016/j.jqsrt.2020.106936

Title: Where is the Machine Looking? : Locating Discriminative Light-Scattering Features by Class-Activation Mapping
Author: Piedra, Patricio; Gobert, Christian; Kalume, Aimable; Pan, Yong-Le; Kocifaj, Miroslav; Muinonen, Karri; Penttilä, Antti; Zubko, Evgenij; Videen, Gorden
Contributor organization: Department of Physics
Planetary-system research
Particle Physics and Astrophysics
Date: 2020-05
Language: eng
Number of pages: 8
Belongs to series: Journal of Quantitative Spectroscopy & Radiative Transfer
ISSN: 0022-4073
DOI: https://doi.org/10.1016/j.jqsrt.2020.106936
URI: http://hdl.handle.net/10138/315734
Abstract: We explore a technique called class-activation mapping (CAM) to investigate how a Machine Learning (ML) architecture learns to classify particles based on their light-scattering signals. We release our code, and also find that different regions of the light-scattering signals play different roles in ML classification. These regions depend on the type of particles being classified and on the nature of the data obtained and trained. For instance, the Mueller-matrix elements S-11*, S-1(2)* and S-21* had the greatest classification activation in the diffraction region. Linear polarization elements S-1(2)* and S-21* were most accurate in the backscattering region for clusters of spheres and spores, and was most accurate in the diffraction region for other particle classes. The CAM technique was able to highlight light-scattering angles that maximize the potential for discrimination of similar particle classes. Such information is useful for designing detector systems to classify particles where limited space or resources are available, including flow cytometry and satellite remote sensing. (C) 2020 The Authors. Published by Elsevier Ltd.
Subject: 114 Physical sciences
Light scattering
Deep learning
SHAPE
FLUORESCENCE
AIRBORNE PARTICLES
SIZE
CLASSIFICATION
PATTERNS
Peer reviewed: Yes
Rights: cc_by_nc_nd
Usage restriction: openAccess
Self-archived version: acceptedVersion


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