Mário A T Figueiredo

Mário A T Figueiredo
University of Lisbon | UL · Instituto de Telecomunicações

PhD

About

354
Publications
46,747
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24,963
Citations
Additional affiliations
January 1994 - September 2015
University of Lisbon
Position
  • Professor (Full)

Publications

Publications (354)
Preprint
In recent years, machine learning algorithms have become ubiquitous in a multitude of high-stakes decision-making applications. The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within. A biased model can then make decisions that disproportionately harm certain group...
Preprint
The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within. A biased model can then make decisions that disproportionately harm certain groups in society. Much work has been devoted to measuring unfairness in static ML environments, but not in dynamic, performative pre...
Preprint
Human-AI collaboration (HAIC) in decision-making aims to create synergistic teaming between human decision-makers and AI systems. Learning to Defer (L2D) has been presented as a promising framework to determine who among humans and AI should take which decisions in order to optimize the performance and fairness of the combined system. Nevertheless,...
Article
This paper evaluates adaptive Q-learning (AQL) and single-partition adaptive Q-learning (SPAQL), two algorithms for efficient model-free episodic reinforcement learning (RL), in two classical control problems (Pendulum and CartPole). AQL adaptively partitions the state–action space of a Markov decision process (MDP), while learning the control poli...
Article
Full-text available
Radiomics is emerging as a promising tool to extract quantitative biomarkers—called radiomic features—from medical images, potentially contributing to the improvement in diagnosis and treatment of oncological patients. However, technical limitations might impair the reliability of radiomic features and their ability to quantify clinically relevant...
Preprint
Full-text available
Recent work has shown promising results in causal discovery by leveraging interventional data with gradient-based methods, even when the intervened variables are unknown. However, previous work assumes that the correspondence between samples and interventions is known, which is often unrealistic. We envision a scenario with an extensive dataset sam...
Article
Detecting diseases, such as cancer, from from gene expression data has assumed great importance and is a very active area of research. Today, many gene expression datasets are publicly available, which consist of microarray data with information on the activation (or not) of thousands of genes, in sets of patients that have (or not) a certain disea...
Preprint
Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e.g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation). Distributions in each of these families have fixed support. In contrast, for finite domains, there has been recent works on s...
Preprint
In recent work, we proposed a distributed Picard iteration (DPI) that allows a set of agents, linked by a communication network, to find a fixed point of a locally contractive (LC) map that is the average of individual maps held by said agents. In this work, we build upon the DPI and its local linear convergence (LLC) guarantees to make several con...
Article
Full-text available
Purpose: Radiogenomics offers a potential virtual and noninvasive biopsy. However, radiogenomics models often suffer from generalizability issues, which cause a performance degradation on unseen data. In MRI, differences in the sequence parameters, manufacturers, and scanners make this generalizability issue worse. Such image acquisition informatio...
Preprint
Full-text available
The Picard iteration is widely used to find fixed points of locally contractive (LC) maps. This paper extends the Picard iteration to distributed settings; specifically, we assume the map of which the fixed point is sought to be the average of individual (not necessarily LC) maps held by a set of agents linked by a sparse communication network. An...
Article
The need for feature selection (FS) techniques is central in many machine learning and pattern recognition problems. FS is a vast research field and therefore we now have many FS techniques proposed in the literature, applied in the context of quite different problems. Some of these FS techniques follow the relevance-redundancy (RR) framework to se...
Article
Recounts the career and contributions of Josa Manuel Bioucas-Dias.
Preprint
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Recurrent neural networks are a standard building block in numerous machine learning domains, from natural language processing to time-series classification. While their application has grown ubiquitous, understanding of their inner workings is still lacking. In practice, the complex decision-making in these models is seen as a black-box, creating...
Chapter
This paper introduces an approach to missing data imputation based on deep auto-encoder models, adequate to high-dimensional data exhibiting complex dependencies, such as images. The method exploits the properties of the vector field associated to an auto-encoder, which allows to approximate the gradient of the log-density from its reconstruction e...
Preprint
Full-text available
This paper evaluates adaptive Q-learning (AQL) and single-partition adaptive Q-learning (SPAQL), two algorithms for efficient model-free episodic reinforcement learning (RL), in two classical control problems (Pendulum and Cartpole). AQL adaptively partitions the state-action space of a Markov decision process (MDP), while learning the control poli...
Article
Purpose: To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features. Methods: T2 -weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features w...
Article
Full-text available
Interferometric phase (InPhase) estimation, that is, the denoising of modulo-2π phase images from sinusoidal 2π-periodic and noisy observations, is a challenging inverse problem with wide applications in many coherent imaging techniques. This paper introduces a novel approach to InPhase restoration based on an external data set and importance sampl...
Preprint
In the past few years, deep generative models, such as generative adversarial networks \autocite{GAN}, variational autoencoders \autocite{vaepaper}, and their variants, have seen wide adoption for the task of modelling complex data distributions. In spite of the outstanding sample quality achieved by those early methods, they model the target distr...
Article
This article proposes a denoiser for hyperspectral (HS) images that consider, not only spatial features, but also spectral features. The method starts by projecting the noisy (observed) HS data onto a lower dimensional subspace and then learns a Gaussian mixture model (GMM) from 3-D patches or blocks extracted from the projected data cube. Afterwar...
Preprint
This paper introduces single-partition adaptive Q-learning (SPAQL), an algorithm for model-free episodic reinforcement learning (RL), which adaptively partitions the state-action space of a Markov decision process (MDP), while simultaneously learning a time-invariant policy (i. e., the mapping from states to actions does not depend explicitly on th...
Preprint
Full-text available
Artificial neural networks, one of the most successful approaches to supervised learning, were originally inspired by their biological counterparts. However, the most successful learning algorithm for artificial neural networks, backpropagation, is considered biologically implausible. We contribute to the topic of biologically plausible neuronal le...
Preprint
Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e.g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation). Distributions in each of these families have fixed support. In contrast, for finite domains, there has been recent work on sp...
Preprint
In this study we investigated the repeatability and reproducibility of radiomic features extracted from MRI images and provide a workflow to identify robust features. 2D and 3D T$_2$-weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of the...
Article
In this paper, we introduce a neural network framework for semi-supervised clustering with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose semi-supervised clustering into two simpler classification tasks: the first stage uses a pair of Siamese neural networks to label the unlabeled pairs of points a...
Preprint
In this paper, we introduce a neural network framework for semi-supervised clustering (SSC) with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose SSC into two simpler classification tasks/stages: the first stage uses a pair of Siamese neural networks to label the unlabeled pairs of points as must-lin...
Article
Full-text available
Purpose of Review This literature review aims to gather the relevant works published on the topic of Radiomics in Rectal Cancer. Research on this topic has focused on finding predictors of rectal cancer staging and chemoradiation treatment response from medical images. The methods presented may, in principle, aid clinicians with the appropriate tre...
Article
Full-text available
Resumo: O presente estudo buscou avaliar se as aquisições oriundas dos processos de compras de materiais hospitalares através do pregão eletrônico, em hospital universitário do Rio de Janeiro, atende ao princípio de segurança para realização dos procedimentos terapêuticos primordiais na recuperação dos pacientes. Constatou-se a necessidade de se re...
Article
Full-text available
Interferometric phase (InPhase) images, acquired by phase imaging systems, often suffer from two major degradations: 1) phase wrapping, caused by the sinusoidal 2π-periodic sensing mechanism, and 2) noise, introduced by the acquisition process or the system. This work focuses on InPhase denoising, which is a fundamental restoration step to many pos...
Article
Full-text available
Linear hyperspectral unmixing (HU) aims at factoring the observation matrix into an endmember matrix and an abundance matrix. Linear HU via variational minimum volume (MV) regularization has recently received considerable attention in the remote sensing and machine learning areas, mainly owing to its robustness against the absence of pure pixels. W...
Article
Full-text available
Most of the existing single-image blind deblurring methods are tailored for natural images. However, in many important applications (e.g., document analysis, forensics), the image being recovered belongs to a specific class (e.g., text, faces, fingerprints) or contains two or more classes. To deal with these images, we propose a class-adapted blind...
Article
Leveraging current state-of-the-art denoisers to tackle other inverse problems in imaging is a challenging task, which has recently been the topic of significant research effort. In this paper, we present several contributions to this research front, based on two fundamental building blocks: (i) the recently-proposed plug-and-play framework, which...
Article
This paper introduces a new approach to patch-based image restoration based on external datasets and importance sampling. The minimum mean squared error (MMSE) estimate of the image patches, the computation of which requires solving a multidimensional (typically intractable) integral, is approximated using samples from an external dataset. The ne...
Preprint
Full-text available
Interferometric phase (InPhase) imaging is an important part of many present-day coherent imaging technologies. Often in such imaging techniques, the acquired images, known as interferograms, suffer from two major degradations: 1) phase wrapping caused by the fact that the sensing mechanism can only measure sinusoidal $2\pi$-periodic functions of t...
Article
We propose a new approach to image fusion, inspired by the recent plug-and-play (PnP) framework. In PnP, a denoiser is treated as a black-box and plugged into an iterative algorithm, taking the place of the proximity operator of some convex regularizer, which is formally equivalent to a denoising operation. This approach offers flexibility and exce...
Preprint
The Conditional Random Field as a Recurrent Neural Network layer is a recently proposed algorithm meant to be placed on top of an existing Fully-Convolutional Neural Network to improve the quality of semantic segmentation. In this paper, we test whether this algorithm, which was shown to improve semantic segmentation for 2D RGB images, is able to i...
Preprint
This paper proposes a general framework for internal patch-based image restoration based on Conditional Random Fields (CRF). Unlike related models based on Markov Random Fields (MRF), our approach explicitly formulates the posterior distribution for the entire image. The potential functions are taken as proportional to the product of a likelihood a...
Preprint
Full-text available
This paper introduces a new approach to patch-based image restoration based on external datasets and importance sampling. The Minimum Mean Squared Error (MMSE) estimate of the image patches, the computation of which requires solving a multidimensional (typically intractable) integral, is approximated using samples from an external dataset. The new...
Article
In this paper, we present a novel clustering scheme based on binary embeddings, which provides compact and informative binary representations of high-dimensional objects. The binary representations are obtained with a collection of one-class classifiers learned from (pseudo) randomly selected points in the dataset. To cluster the binary representat...
Article
Full-text available
Scanning tunnelling microscopy (STM) was used to induce conformational molecular switching on a self-assembled monolayer of zinc-octaethylporphyrin on a graphite/tetradecane interface at room temperature. A reversible conformational change controlled by applying a tip voltage was observed. Consecutive STM images acquired at alternating tip voltages...
Article
Full-text available
In this paper, we address the problem of denoising images degraded by Poisson noise. We propose a new patch-based approach based on best linear prediction to estimate the underlying clean image. A simplified prediction formula is derived for Poisson observations, which requires the covariance matrix of the underlying clean patch. We use the assumpt...
Conference Paper
Full-text available
Deep neural networks (DNNs) usually contain millions, maybe billions, of parameters/weights, making both storage and computation very expensive. This has motivated a large body of work to reduce the complexity of the neural network by using sparsity-inducing regularizers. Another well-known approach for controlling the complexity of DNNs is paramet...
Article
Full-text available
The recently proposed plug-and-play (PnP) framework allows leveraging recent developments in image denoising to tackle other, more involved, imaging inverse problems. In a PnP method, a black-box denoiser is plugged into an iterative algorithm, taking the place of a formal denoising step that corresponds to the proximity operator of some convex reg...
Article
Full-text available
Blind image deblurring (BID) is an ill-posed inverse problem, usually addressed by imposing prior knowledge on the (unknown) image and on the blurring filter. Most of the work on BID has focused on natural images, using image priors based on statistical properties of generic natural images. However, in many applications, it is known that the image...
Article
Biclustering refers to the problem of simultaneously clustering the rows and columns of a given data matrix, with the goal of obtaining submatrices where the selected rows present a coherent behaviour in the selected columns, and vice-versa. To face this intrinsically difficult problem, we propose a novel generative model, where biclustering is app...
Article
In this paper, we propose a new image denoising method, tailored to specific classes of images, assuming that a dataset of clean images of the same class is available. Similarly to the non-local means (NLM) algorithm, the proposed method computes a weighted average of non-local patches, which we interpret under the importance sampling framework. Th...
Article
The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters. We study distributed ADMM methods that boost performance by using different fine-tuned algorithm parameters on each worker node. We present a O(1/k) c...
Article
In this paper, we address the problem of recovering images degraded by Poisson noise, where the image is known to belong to a specific class. In the proposed method, a dataset of clean patches from images of the class of interest is clustered using multivariate Gaussian distributions. In order to recover the noisy image, each noisy patch is assigne...
Conference Paper
Full-text available
Feature extraction is a crucial step in any computer aided diagnosis (CAD) system for melanoma diagnosis. Therefore, it is important to select features that are able to efficiently characterize the properties of the different types of lesions. Local features that separately characterize and distinguish different regions of the lesions have been sho...
Article
Many modern computer vision and machine learning applications rely on solving difficult optimization problems that involve non-differentiable objective functions and constraints. The alternating direction method of multipliers (ADMM) is a widely used approach to solve such problems. Relaxed ADMM is a generalization of ADMM that often achieves bette...
Article
In global models/priors (for example, using wavelet frames), there is a well known analysis vs synthesis dichotomy in the way signal/image priors are formulated. In patch-based image models/priors, this dichotomy is also present in the choice of how each patch is modeled. This paper shows that there is another analysis vs synthesis dichotomy, in te...
Article
Full-text available
This paper tackles a hyperspectral data fusion problem, using the so-called plug-and-play approach, which combines an ADMM algorithm with a denoiser based on a Gaussian mixture prior. We build upon the concept of scene-adapted prior where, as the name suggests, we learn a model that is targeted to the specific scene being imaged, and show state-of-...
Conference Paper
This paper addresses the problem of fusing hyperspectral (HS) images of low spatial resolution and multispectral (MS) images of high spatial resolution into images of high spatial and spectral resolution. By assuming that the target image lives in a low dimensional subspace, the problem is formulated with respect to the latent representation coeffi...
Article
Full-text available
The alternating direction method of multipliers (ADMM) is a common optimization tool for solving constrained and non-differentiable problems. We provide an empirical study of the practical performance of ADMM on several nonconvex applications, including l0 regularized linear regression, l0 regularized image denoising, phase retrieval, and eigenvect...
Article
The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid....
Article
Full-text available
In this article, we present a denoising algorithm to improve the interpretation and quality of scanning tunneling microscopy (STM) images. Given the high level of self-similarity of STM images, we propose a denoising algorithm by reformulating the true estimation problem as a sparse regression, often termed sparse coding. We introduce modifications...
Conference Paper
State-of-the-art algorithms for imaging inverse problems (namely deblurring and reconstruction) are typically iterative, involving a de-noising operation as one of its steps. Using a state-of-the-art denoising method in this context is not trivial, and is the focus of current work. Recently, we have proposed to use a class-adapted denoiser (patch-b...
Article
Biclustering represents an intrinsically complex problem, where the aim is to perform a simultaneous row- and column-clustering of a given data matrix. Some recent approaches model this problem using factor graphs, so to exploit their ability to open the door to efficient optimization approaches for well designed function decompositions. However, w...
Conference Paper
Segmentation is one of the central problems in image analysis, where the goal is to partition the image domain into regions exhibiting some sort of homogeneity. Most often, the partition is obtained by solving a combinatorial optimization problem, which is, in general, NP-hard. In this paper, we follow an alternative approach, using a Bayesian form...
Article
Full-text available
In this paper, we propose an automatic way to select class-adapted Gaussian mixture priors, in image denoising or deblurring tasks. We follow the Bayesian perspective and use a maximum a posteriori criterion to determine the model that best explains each observed patch. In situations where it is not possible to learn a model from the observed image...
Article
The alternating direction method of multipliers (ADMM) is a versatile tool for solving a wide range of constrained optimization problems, with differentiable or non-differentiable objective functions. Unfortunately, its performance is highly sensitive to a penalty parameter, which makes ADMM often unreliable and hard to automate for a non-expert us...