5 citations · 8 across the 10 of their papers we have counts for
10 papers
CDPM-Align: Multi-Scale Guidance-Aligned Diffusion Pretraining for Robust Few-Shot Anatomical Landmark Detection
Roberto Di Via, Irina Voiculescu, Francesca Odone +1
Anatomical landmark detection is a fundamental task in medical image analysis supporting a wide range of diagnostic and interventional workflows. Although recent methods have achie…
Automated Landmark Detection for assessing hip conditions: A Cross-Modality Validation of MRI versus X-ray
Roberto Di Via, Vito Paolo Pastore, Francesca Odone +2
Many clinical screening decisions are based on angle measurements. In particular, FemoroAcetabular Impingement (FAI) screening relies on angles traditionally measured on X-rays. Ho…
USV Obstacles Detection and Tracking in Marine Environments
Yara AlaaEldin, Enrico Simetti, Francesca Odone
Developing a robust and effective obstacle detection and tracking system for Unmanned Surface Vehicle (USV) at marine environments is a challenging task. Research efforts have been…
Disentangled representations of microscopy images
Jacopo Dapueto, Vito Paolo Pastore, Nicoletta Noceti +1
Microscopy image analysis is fundamental for different applications, from diagnosis to synthetic engineering and environmental monitoring. Modern acquisition systems have granted t…
Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on Aerial Images
Yara AlaaEldin, Francesca Odone
Understanding the geometric and semantic properties of the scene is crucial in autonomous navigation and particularly challenging in the case of Unmanned Aerial Vehicle (UAV) navig…
Diffusing DeBias: Synthetic Bias Amplification for Model Debiasing
Massimiliano Ciranni, Vito Paolo Pastore, Roberto Di Via +3
Deep learning model effectiveness in classification tasks is often challenged by the quality and quantity of training data whenever they are affected by strong spurious correlation…
Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images
Roberto Di Via, Francesca Odone, Vito Paolo Pastore
Deep neural networks have been extensively applied in the medical domain for various tasks, including image classification, segmentation, and landmark detection. However, their app…
Looking at Model Debiasing through the Lens of Anomaly Detection
Vito Paolo Pastore, Massimiliano Ciranni, Davide Marinelli +2
It is widely recognized that deep neural networks are sensitive to bias in the data. This means that during training these models are likely to learn spurious correlations between…
Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?
Roberto Di Via, Matteo Santacesaria, Francesca Odone +1
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…
Top-Tuning: a study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods
Paolo Didier Alfano, Vito Paolo Pastore, Lorenzo Rosasco +1
The impressive performance of deep learning architectures is associated with a massive increase in model complexity. Millions of parameters need to be tuned, with training and infe…