5 citations · 14 across the 20 of their papers we have counts for
4 papers · 1 filter
Say My Name: a Model's Bias Discovery Framework
Massimiliano Ciranni, Luca Molinaro, Carlo Alberto Barbano +4
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 t…
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…