39 citations · 56 across the 9 of their papers we have counts for
9 papers
Parameter-free entropy-regularized multi-view clustering with hierarchical feature selection
Kristina P. Sinaga, Sara Colantonio, Miin-Shen Yang
Multi-view clustering faces critical challenges in automatically discovering patterns across heterogeneous data while managing high-dimensional features and eliminating irrelevant…
Connectivity-Inspired Network for Context-Aware Recognition
Gianluca Carloni, Sara Colantonio
The aim of this paper is threefold. We inform the AI practitioner about the human visual system with an extensive literature review; we propose a novel biologically motivated neura…
CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning
Gianluca Carloni, Sotirios A Tsaftaris, Sara Colantonio
Due to domain shift, deep learning image classifiers perform poorly when applied to a domain different from the training one. For instance, a classifier trained on chest X-ray (CXR…
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification
Eva Pachetti, Sotirios A. Tsaftaris, Sara Colantonio
Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data. We…
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI
Gianluca Carloni, Eva Pachetti, Sara Colantonio
In this paper, we present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutio…
The role of causality in explainable artificial intelligence
Gianluca Carloni, Andrea Berti, Sara Colantonio
Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation shar…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
A Systematic Review of Few-Shot Learning in Medical Imaging
Eva Pachetti, Sara Colantonio
The lack of annotated medical images limits the performance of deep learning models, which usually need large-scale labelled datasets. Few-shot learning techniques can reduce data…
Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results
Gianluca Carloni, Sara Colantonio
We present a novel technique to discover and exploit weak causal signals directly from images via neural networks for classification purposes. This way, we model how the presence o…