Luca Rossi — accepted AI-conference papers

2025

BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology

Amaya Gallagher-Syed, Henry Senior, Omnia Alwazzan, Elena Pontarini, Michele Bombardieri, Costantino Pitzalis +4

CVPR 2025poster

The development of biologically interpretable and explainable models remains a key challenge in computational pathology, particularly for multistain immunohistochemistry (IHC) analysis. We present BioX-CPath, an explainable graph neural network architecture for whole slide image (WSI) classification…

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2025

Generating Graphs via Spectral Diffusion

Giorgia Minello, Alessandro Bicciato, Luca Rossi, Andrea Torsello, Luca Cosmo

ICLR 2025poster

In this paper, we present GGSD, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process. Specifically, we propose to use a denoising model to sample eigenvectors and eigenvalues from which we can reconstruct the graph Laplacian a…

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2020

GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild

Umberto Michieli, Edoardo Borsato, Luca Rossi, Pietro Zanuttigh

ECCV 2020poster

The semantic segmentation of parts of objects in the wild is a challenging task in which multiple instances of objects and multiple parts within those objects must be detected in the scene. This problem remains nowadays very marginally explored, despite its fundamental importance towards detailed ob…

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2020

The Average Mixing Kernel Signature

Luca Cosmo, Giorgia Minello, Michael Bronstein, Luca Rossi, Andrea Torsello

ECCV 2020poster

We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for points on non-rigid three-dimensional shapes based on the average mixing kernel and continuous-time quantum walks. The average mixing kernel holds information on the average transition probabilities of a quantum walk betw…

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2015

An Aligned Subtree Kernel for Weighted Graphs

Lu Bai, Luca Rossi, Zhihong Zhang, Edwin Hancock

ICML 2015poster

In this paper, we develop a new entropic matching kernel for weighted graphs by aligning depth-based representations. We demonstrate that this kernel can be seen as an \textbfaligned subtree kernel that incorporates explicit subtree correspondences, and thus addresses the drawback of neglecting the…

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