Vito Paolo Pastore

Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images

Deep neural networks have been extensively applied in the medical domain for various tasks, including image classification, segmentation, and landmark detection. However, their application is...

Say My Name: a Model's Bias Discovery Framework

In the last few years, due to the broad applicability of deep learning to downstream tasks and end-to-end training capabilities, increasingly more concerns about potential biases to specific,...

DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images

Motion artifacts remain a significant challenge in Magnetic Resonance Imaging (MRI), compromising diagnostic quality and potentially leading to misdiagnosis or repeated scans. Existing deep...

Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?

In recent years, deep learning has emerged as a promising technique for medical image analysis. However, this application domain is likely to suffer from a limited availability of large public...

Efficient Unsupervised Learning for Plankton Images

Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect...

An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis

This study provides a data-driven analysis of a novel dataset of single-cell Anion Exchange Membrane water electrolyzers (AEMWE), operated under constant current load across multiple heterogeneous...

Revisiting Diffusion Fine-Tuning for Unsupervised Domain Adaptation

Diffusion-based unsupervised domain adaptation (UDA) improves cross-domain transfer by generating target-specific synthetic data for downstream adaptation. Existing methods are largely designed...

TReViS: Temporal Repetition Structure Aware Video Synthesis for Self-supervised Repetitive Action Counting

Fully supervised repetitive action counting (RAC) has achieved strong performance, but requires dense temporal annotations that are costly and difficult to scale. We propose TReViS, a...

Are X-Ray Landmark Detection Models Fair? A Preliminary Assessment and Mitigation Strategy

Incorporating Diagnostic Prior with Segmentation: A Deep Learning Pipeline for the Automatic Classification of Autoimmune Bullous Skin Diseases

Assessing the Use of Diffusion Models for Motion Artifact Correction in Brain MRI

A Pair-Weighing Strategy for Enhancing Clip Zero-Shot Classification for Chest X-Rays

Self-Supervised Multi-Modal Learning for Accurate MRI Multiple Sclerosis Segmentation

Automated Landmark Detection for Assessing Hip Conditions: A Cross-Modality Validation of Mri Versus X-Ray

Diffusing DeBias: Synthetic Bias Amplification for Model Debiasing

GCK-Maps: A Scene Unbiased Representation for Efficient Human Action Recognition

An Unsupervised Learning Approach to Resolve Phenotype to Genotype Mapping in Budding Yeasts Vacuoles

A Handful of Data: Evaluating Few-Shot Incremental Landmark Detection