9 papers
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…
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…
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…
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…
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…
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…