10 citations · 12 across the 8 of their papers we have counts for
8 papers
Pathology Foundation Models are Scanner Sensitive: Benchmark and Mitigation with Contrastive ScanGen Loss
Gianluca Carloni, Biagio Brattoli, Seongho Keum +4
Computational pathology (CPath) has shown great potential in mining actionable insights from Whole Slide Images (WSIs). Deep Learning (DL) has been at the center of modern CPath, a…
Human-aligned Deep Learning: Explainability, Causality, and Biological Inspiration
Gianluca Carloni
This work aligns deep learning (DL) with human reasoning capabilities and needs to enable more efficient, interpretable, and robust image classification. We approach this from thre…
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…
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
Yuyang Xue, Yuning Du, Gianluca Carloni +3
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner. Undersampling of the -space is employed to reduce…
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…
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…