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SPOTNET - LEARNED ITERATIONS FOR CELL DETECTION IN IMAGE-BASED IMMUNOASSAYS
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.
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0001-6630-243X
2019 (English)In: 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019), IEEE , 2019, p. 1023-1027Conference paper, Published paper (Refereed)
Abstract [en]

Accurate cell detection and counting in the image-based ELISpot and FluoroSpot immunoassays is a challenging task. Recently proposed methodology matches human accuracy by leveraging knowledge of the underlying physical process of these assays and using proximal optimization methods to solve an inverse problem. Nonetheless, thousands of computationally expensive iterations are often needed to reach a near-optimal solution. In this paper, we exploit the structure of the iterations to design a parameterized computation graph, SpotNet, that learns the patterns embedded within several training images and their respective cell information. Further, we compare SpotNet to a convolutional neural network layout customized for cell detection. We show empirical evidence that, while both designs obtain a detection performance on synthetic data far beyond that of a human expert, SpotNet is easier to train and obtains better estimates of particle secretion for each cell.

Place, publisher, year, edition, pages
IEEE , 2019. p. 1023-1027
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928
Keywords [en]
Source localization, Immunoassays, Convolutional sparse coding, Artificial neural networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-261055DOI: 10.1109/ISBI.2019.8759568ISI: 000485040000216ISBN: 978-1-5386-3641-1 (print)OAI: oai:DiVA.org:kth-261055DiVA, id: diva2:1356659
Conference
16th IEEE International Symposium on Biomedical Imaging (ISBI), APR 08-11, 2019, Venice, ITALY
Note

QC 20191002

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved

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del Aguila Pla, PolSaxena, ViditJaldén, Joakim

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