29 citations · 129 across the 27 of their papers we have counts for
28 papers
Robust confidence intervals for generalized linear models
Andrea Panarotto, Riccardo De Santis, Livio Finos
Reliable uncertainty quantification is a central challenge in the analysis of modern biomedical data, where complex sources of variability often violate standard modeling assumptio…
Sign-Flipping Score Tests for Fixed Effects in Multivariate Clustered Data
Angela Andreella, Livio Finos
Standard parametric inference on fixed effects in multivariate linear mixed models can be sensitive to covariance misspecification, particularly with multivariate responses. We pro…
The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: a preliminary study
Federico Del Pup, Andrea Zanola, Louis Fabrice Tshimanga +3
Deep learning is significantly advancing the analysis of electroencephalography (EEG) data by effectively discovering highly nonlinear patterns within the signals. Data partitionin…
HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification
Valentina Vadori, Jean-Marie Graïc, Antonella Peruffo +3
Precise segmentation and classification of cell instances are vital for analyzing the tissue microenvironment in histology images, supporting medical diagnosis, prognosis, treatmen…
Automated Classification of Cell Shapes: A Comparative Evaluation of Shape Descriptors
Valentina Vadori, Antonella Peruffo, Jean-Marie Graïc +2
This study addresses the challenge of classifying cell shapes from noisy contours, such as those obtained through cell instance segmentation of histological images. We assess the p…
Conformal inference for cell type annotation with graph-structured constraints
Daniela Corbetta, Livio Finos, Ludwig Geistlinger +1
Conformal prediction is a framework for constructing prediction sets for machine learning models, relying solely on the exchangeability of training and test data and without requir…
CISCA and CytoDArk0: a Cell Instance Segmentation and Classification method for histo(patho)logical image Analyses and a new, open, Nissl-stained dataset for brain cytoarchitecture studies
Valentina Vadori, Jean-Marie Graïc, Antonella Peruffo +3
Delineating and classifying individual cells in microscopy tissue images is inherently challenging yet remains essential for advancements in medical and neuroscientific research. I…
Permutation-based multiple testing when fitting many generalized linear models
Riccardo De Santis, Jelle J. Goeman, Samuel Davenport +2
In many applied sciences a popular analysis strategy for high-dimensional data is to fit many multivariate generalized linear models in parallel. This paper presents a novel approa…
Robust Inference for Generalized Linear Mixed Models: An Approach Based on Score Sign Flipping
Angela Andreella, Jelle Goeman, Jesse Hemerik +1
Despite the versatility of generalized linear mixed models in handling complex experimental designs, they often suffer from misspecification and convergence problems. This makes in…
Revealing Cortical Layers In Histological Brain Images With Self-Supervised Graph Convolutional Networks Applied To Cell-Graphs
Valentina Vadori, Antonella Peruffo, Jean-Marie Graïc +3
Identifying cerebral cortex layers is crucial for comparative studies of the cytoarchitecture aiming at providing insights into the relations between brain structure and function a…
A novel CFA+EFA model to detect aberrant respondents
Niccolò Cao, Livio Finos, Luigi Lombardi +1
Aberrant respondents are common but yet extremely detrimental to the quality of social surveys or questionnaires. Recently, factor mixture models have been employed to identify ind…
NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images
Valentina Vadori, Antonella Peruffo, Jean-Marie Graïc +3
Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of segmenting individual cells, an e…
Procrustes-based distances for exploring between-matrices similarity
Angela Andreella, Riccardo De Santis, Anna Vesely +1
The statistical shape analysis called Procrustes analysis minimizes the distance between matrices by similarity transformations. The method returns a set of optimal orthogonal matr…
MR-NOM: Multi-scale Resolution of Neuronal cells in Nissl-stained histological slices via deliberate Over-segmentation and Merging
Valentina Vadori, Jean-Marie Graïc, Livio Finos +3
In comparative neuroanatomy, the characterization of brain cytoarchitecture is critical to a better understanding of brain structure and function, as it helps to distill informatio…
Post-selection Inference in Multiverse Analysis (PIMA): an inferential framework based on the sign flipping score test
Paolo Girardi, Anna Vesely, Daniël Lakens +4
When analyzing data researchers make some decisions that are either arbitrary, based on subjective beliefs about the data generating process, or for which equally justifiable alter…
Enhanced hyperalignment via spatial prior information
Angela Andreella, Livio Finos, Martin A Lindquist
Functional alignment between subjects is an important assumption of functional magnetic resonance imaging (fMRI) group-level analysis. However, it is often violated in practice, ev…
Inference in generalized linear models with robustness to misspecified variances
Riccardo De Santis, Jelle J. Goeman, Jesse Hemerik +2
Generalized linear models usually assume a common dispersion parameter, an assumption that is seldom true in practice. Consequently, standard parametric methods may suffer apprecia…
Resampling-Based Multisplit Inference for High-Dimensional Regression
Anna Vesely, Jelle J. Goeman, Livio Finos
We propose a novel resampling-based method to construct an asymptotically exact test for any subset of hypotheses on coefficients in high-dimensional linear regression. It can be e…
Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
Anna Stakia, Tommaso Dorigo, Giovanni Banelli +22
Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multiv…
Permutation-Based True Discovery Guarantee by Sum Tests
Anna Vesely, Livio Finos, Jelle J. Goeman
Sum-based global tests are highly popular in multiple hypothesis testing. In this paper we propose a general closed testing procedure for sum tests, which provides lower confidence…
Permutation-based true discovery proportions for functional Magnetic Resonance Imaging cluster analysis
Angela Andreella, Jesse Hemerik, Wouter Weeda +2
We propose a permutation-based method for testing a large collection of hypotheses simultaneously. Our method provides lower bounds for the number of true discoveries in any select…
Procrustes analysis for high-dimensional data
Angela Andreella, Livio Finos
The Procrustes-based perturbation model (Goodall, 1991) allows minimization of the Frobenius distance between matrices by similarity transformation. However, it suffers from non-id…
Permutation testing in high-dimensional linear models: an empirical investigation
Jesse Hemerik, Magne Thoresen, Livio Finos
Permutation testing in linear models, where the number of nuisance coefficients is smaller than the sample size, is a well-studied topic. The common approach of such tests is to pe…
(Mis)Information Operations: An Integrated Perspective
Matteo Cinelli, Mauro Conti, Livio Finos +6
The massive diffusion of social media fosters disintermediation and changes the way users are informed, the way they process reality, and the way they engage in public debate. The…