| 1. |
- del Aguila Pla, Pol, 1990-, et al.
(författare)
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Cell detection by functional inverse diffusion and non-negative group sparsity – Part I: Modeling and Inverse Problems
- 2018
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Ingår i: IEEE Transactions on Signal Processing. - : IEEE. - 1053-587X .- 1941-0476. ; 66:20, s. 5407-5421
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Tidskriftsartikel (refereegranskat)abstract
- 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 first part, we start by presenting a physical partial differential equations (PDE) model up to image acquisition for these biochemical assays. Then, we use the PDEs' Green function to derive a novel parametrization of the acquired images. This parametrization allows us to propose a functional optimization problem to address inverse diffusion. In particular, we propose a non-negative group-sparsity regularized optimization problem with the goal of localizing and characterizing the biological cells involved in the said assays. We continue by proposing a suitable discretization scheme that enables both the generation of synthetic data and implementable algorithms to address inverse diffusion. We end Part I by providing a preliminary comparison between the results of our methodology and an expert human labeler on real data. Part II is devoted to providing an accelerated proximal gradient algorithm to solve the proposed problem and to the empirical validation of our methodology.
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| 2. |
- del Aguila Pla, Pol, 1990-, et al.
(författare)
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Cell detection by functional inverse diffusion and non-negative group sparsity – Part II: Proximal optimization and Performance evaluation
- 2018
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Ingår i: IEEE Transactions on Signal Processing. - : IEEE. - 1053-587X .- 1941-0476. ; 66:20, s. 5422-5437
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- del Aguila Pla, Pol, 1990-, et al.
(författare)
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Cell detection on image-based immunoassays
- 2018
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Ingår i: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). - : IEEE. - 9781538636367 - 9781538636374 - 9781538636350 ; , s. 431-435
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Konferensbidrag (refereegranskat)abstract
- Cell detection and counting in the image-based ELISPOT and Fluorospot immunoassays is considered a bottleneck.The task has remained hard to automatize, and biomedical researchers often have to rely on results that are not accurate.Previously proposed solutions are heuristic, and data-based solutions are subject to a lack of objective ground truth data. In this paper, we analyze a partial differential equations model for ELISPOT, Fluorospot, and assays of similar design. This leads us to a mathematical observation model forthe images generated by these assays. We use this model to motivate a methodology for cell detection. Finally, we provide a real-data example that suggests that this cell detection methodology and a human expert perform comparably.
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| 4. |
- del Aguila Pla, Pol, 1990-, et al.
(författare)
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Clock synchronization over networks - Identifiability of the sawtooth model
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we analyze the two-node joint clocksynchronization and ranging problem. We focus on the case of nodes that employ time-to-digital converters to determine the range between them precisely. This specific design leads to a sawtooth model for the captured signal, which has not been studied in detail before from an estimation theory standpoint. In the study of this model, we recover the basic conclusion of a well-known article by Freris, Graham, and Kumar in clock synchronization. Additionally, we discover a surprising identifiability result on the sawtooth signal model: noise improves the theoretical condition of the estimation of the phase and offset parameters. To complete our study, we provide performance references for joint clock synchronization and ranging. In particular, we present the Cramér-Rao lower bounds that correspond to a linearization of our model, as well as a simulation study on the practical performance of basic estimation strategies under realistic parameters. With these performance references, we enable further research in estimation strategies using the sawtooth model and pave the path towards industrial use.
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| 5. |
- del Aguila Pla, Pol, 1990-, et al.
(författare)
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Convolutional group-sparse coding and source localization
- 2018
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Konferensbidrag (refereegranskat)abstract
- In this paper, we present a new interpretation of non-negatively constrained convolutional coding problems as blind deconvolution problems with spatially variant point spread function. In this light, we propose an optimization framework that generalizes our previous work on non-negative group sparsity for convolutional models. We then link these concepts to source localization problems that arise in scientific imaging, and provide a visual example on an image derived from data captured by the Hubble telescope.
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| 6. |
- del Aguila Pla, Pol, 1990-, et al.
(författare)
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Fast generation of LULC maps for temporal studies in North-Western Africa
- 2014
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Ingår i: Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International. - : IEEE conference proceedings. ; , s. 4280-4283
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Konferensbidrag (refereegranskat)abstract
- This paper provides an objective evaluation of six supervised classification techniques and three state of the art features, with the objective of obtaining a single combination of them that provides both robustness and objective performance improvements. As a conclusion, a simple procedure for obtaining LULC maps with four targeted classes is proposed.
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| 7. |
- del Aguila Pla, Pol, 1990-, et al.
(författare)
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Inferences from quantized data - Likelihood logconcavity
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we present to the signal processing community the most general likelihood logconcavity statement for quantized data to date, together with its proof, which has never been published. In particular, we show how Prékopa’s theorem can be used to show that the likelihood for quantized linear models is jointly logconcave with respect to both its location and scale parameter in a broad range of cases. In order to show this result and explain the limitations of the proof technique, we study sets generated by combinations of points with positive semi-definite matrices whose sum is the identity.
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| 8. |
- del Aguila Pla, Pol, 1990-
(författare)
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Inverse problems in signal processing : Functional optimization, parameter estimation and machine learning
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 9. |
- del Aguila Pla, Pol, 1990-, et al.
(författare)
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SpotNet : Learned iterations for cell detection in image-based immunoassays
- 2019
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- 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.
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