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SpotNet :
SpotNet : Learned iterations for cell detection in image-based immunoassays
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- del Aguila Pla, Pol, 1990- (author)
- KTH,Teknisk informationsvetenskap
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- Saxena, Vidit (author)
- KTH,Teknisk informationsvetenskap
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- Jaldén, Joakim, 1976- (author)
- KTH,Teknisk informationsvetenskap
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2019
- 2019
- English.
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In: Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE).
- Related links:
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https://biomedicalim...
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https://kth.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Keyword
- Source localization
- Immunoassays
- Convolutional sparse coding
- Artificial neural networks
- Electrical Engineering
- Elektro- och systemteknik
Publication and Content Type
- ref (subject category)
- kon (subject category)
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