Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Cell detection by functional inverse diffusion and non-negative group sparsity – Part II: Proximal optimization and Performance evaluation
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0003-3054-7210
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0001-6630-243X
2018 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 20, p. 5422-5437Article in journal (Refereed) Published
Abstract [en]

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this second part, we focus on our algorithmic contributions. We provide an algorithm for functional inverse diffusion that solves the variational problem we posed in Part I. As part of the derivation of this algorithm, we present the proximal operator for the non-negative group-sparsity regularizer, which is a novel result that is of interest in itself, also in comparison to previous results on the proximal operator of a sum of functions. We then present a discretized approximated implementation of our algorithm and evaluate it both in terms of operational cell-detection metrics and in terms of distributional optimal-transport metrics.

Place, publisher, year, edition, pages
IEEE, 2018. Vol. 66, no 20, p. 5422-5437
Keywords [en]
Proximal operator, Non-negative group sparsity, Functional optimization, Biomedical imaging, Source localization
National Category
Signal Processing Medical Image Processing
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-233827DOI: 10.1109/TSP.2018.2868256ISI: 000444842400008Scopus ID: 2-s2.0-85052808396OAI: oai:DiVA.org:kth-233827DiVA, id: diva2:1242893
Funder
Swedish Research Council, 2015-04026
Note

QC 20180918

Available from: 2018-08-29 Created: 2018-08-29 Last updated: 2019-08-20Bibliographically approved
In thesis
1. Inverse problems in signal processing: Functional optimization, parameter estimation and machine learning
Open this publication in new window or tab >>Inverse problems in signal processing: Functional optimization, parameter estimation and machine learning
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Inverse problems arise in any scientific endeavor. Indeed, it is seldom the case that our senses or basic instruments, i.e., the data, provide the answer we seek. It is only by using our understanding of how the world has generated the data, i.e., a model, that we can hope to infer what the data imply. Solving an inverse problem is, simply put, using a model to retrieve the information we seek from the data.

In signal processing, systems are engineered to generate, process, or transmit signals, i.e., indexed data, in order to achieve some goal. The goal of a specific system could be to use an observed signal and its model to solve an inverse problem. However, the goal could also be to generate a signal so that it reveals a parameter to investigation by inverse problems. Inverse problems and signal processing overlap substantially, and rely on the same set of concepts and tools. This thesis lies at the intersection between them, and presents results in modeling, optimization, statistics, machine learning, biomedical imaging and automatic control.

The novel scientific content of this thesis is contained in its seven composing publications, which are reproduced in Part II. In five of these, which are mostly motivated by a biomedical imaging application, a set of related optimization and machine learning approaches to source localization under diffusion and convolutional coding models are presented. These are included in Publications A, B, E, F and G, which also include contributions to the modeling and simulation of a specific family of image-based immunoassays. Publication C presents the analysis of a system for clock synchronization between two nodes connected by a channel, which is a problem of utmost relevance in automatic control. The system exploits a specific node design to generate a signal that enables the estimation of the synchronization parameters. In the analysis, substantial contributions to the identifiability of sawtooth signal models under different conditions are made. Finally, Publication D brings to light and proves results that have been largely overlooked by the signal processing community and characterize the information that quantized linear models contain about their location and scale parameters.

Abstract [sv]

Inversa problem uppstår vid alla vetenskapliga undersökningar. Våra sinnen och mätinstrument -rådata -ger faktiskt sällan svaren vi letar efter. Vi behöver då utveckla vår förståelse av hur data genererats, d.v.s., använda en modell, för att kunna dra korrekta slutsatser. Att lösa inversa problem är,enkelt uttryckt, att använda modeller för att få fram den information man vill ha från tillgängliga data.

Signalbehandling handlar om utveckling av system som skapar, behandlar eller överför signaler (d.v.s., indexerade data) för att nå ett visst mål. Ett exempel på mål för en sådant system är att lösa ett inverst problem utifrån den analyserade signalen med hjälp av en modell. Signalbehandling kan dock även handla om att skapa en signal, så att denna avslöjar en parameter för utredning genom ett inverst problem. Inversa problem och signalbehandling är två fält som överlappar i stor utsträckning, och som använder sig av samma koncept och verktyg. Denna avhandling utforskar gränslandet mellan dessa två fält, och presenterar resultat inom modellering, optimering, statistik, maskininlärning, biomedicinsk avbildning och automatisk kontroll.

Det nya vetenskapliga innehållet i den här avhandlingen är baserat på de sju artiklar som återges här i Del II. I fem av dessa artiklar beskrivs ett antal relaterade metoder för optimering och maskininlärning för källokalisering medhjälp av diffusions- och konvolutionsmodellering, med tillämpningar framförallt inom biomedicinsk bildbehandling. Dessa inkluderas i Publikationer A, B,E, F och G, och behandlar också modellering och simulering av en familj av bildbaserade immunkemiska detektionsmetoder. Publikation C presenterar analys av ett system för klocksynkronisering mellan två noder förbundna med en kanal, vilket är ett problem med särskild relevans för automatisk kontroll. Systemet använder en specifik noddesign för att generera en signal som möjliggör skattning av synkroniseringsparametrarna. Analysen bidrar avsevärt till metodiken för att identifiera sågtandsmönstrande signalmodeller under olika förhållanden. Avslutningsvis presenteras i Publikation D resultat som tidigare i stora drag förbisetts inom signalbehandlingsfältet. Här karaktäriseras även den information som kvantiserade linjära modeller innehåller om deras läges- och skalparametrar.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. p. 131
Series
TRITA-EECS-AVL ; 2019:51
Keywords
inverse problems, signal processing, machine learning, biomedical imaging, optimization, proximal optimization, regularization, mathematical modeling, identifiability, likelihood, logconcavity, immunoassays, convolutional coding, functional analysis, abstract inference, learned iterations, unrolled algorithms
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing Probability Theory and Statistics Medical Image Processing Telecommunications
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-256079 (URN)978-91-7873-213-5 (ISBN)
Public defence
2019-09-16, F3, Lindstedtsvägen 26, Stockholm, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

QC 20190820

Available from: 2019-08-20 Created: 2019-08-19 Last updated: 2019-08-20Bibliographically approved

Open Access in DiVA

fulltext(2566 kB)124 downloads
File information
File name FULLTEXT01.pdfFile size 2566 kBChecksum SHA-512
c5ab2f0d74e6eb3062b8ca20120e37e1c4c3501ce66276295b0635559f91d91b1851ac18cdf387bf708d5962c832fe056c22bb27520b9d8fe3868a9e5836e65f
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopusarXiv of accepted version

Authority records BETA

del Aguila Pla, PolJaldén, Joakim

Search in DiVA

By author/editor
del Aguila Pla, PolJaldén, Joakim
By organisation
Information Science and Engineering
In the same journal
IEEE Transactions on Signal Processing
Signal ProcessingMedical Image Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 124 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 362 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf