activity
20172026
most citedEnhancing statistical inference in psychological research via prospective and retrospective design analysis

29 citations · 43 across the 11 of their papers we have counts for

collaborators

19 papers

stat.ME2026

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…

stat.ME2026

Multivariate mixed models with model-free random effects

Angela Andreella, Livio Finos

Linear mixed models are widely used to analyze non-independent data, but inference for fixed effects can be unreliable under misspecification of the random-effects distribution, in…

eess.SP20251 cited

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…

cs.CV2025

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…

cs.CV2024

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…

stat.ME2024

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…