221 citations · 507 across the 42 of their papers we have counts for
7 papers · 1 filter
HEMP: High-order Entropy Minimization for neural network comPression
Enzo Tartaglione, Stéphane Lathuilière, Attilio Fiandrotti +2
We formulate the entropy of a quantized artificial neural network as a differentiable function that can be plugged as a regularization term into the cost function minimized by grad…
EnD: Entangling and Disentangling deep representations for bias correction
Enzo Tartaglione, Carlo Alberto Barbano, Marco Grangetto
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks. There are problems…
Dysplasia grading of colorectal polyps through CNN analysis of WSI
Daniele Perlo, Enzo Tartaglione, Luca Bertero +2
Colorectal cancer is a leading cause of cancer death for both men and women. For this reason, histopathological characterization of colorectal polyps is the major instrument for th…
UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading
Carlo Alberto Barbano, Daniele Perlo, Enzo Tartaglione +4
Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carc…
SeReNe: Sensitivity based Regularization of Neurons for Structured Sparsity in Neural Networks
Enzo Tartaglione, Andrea Bragagnolo, Francesco Odierna +2
Deep neural networks include millions of learnable parameters, making their deployment over resource-constrained devices problematic. SeReNe (Sensitivity-based Regularization of Ne…
A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images
Carlo Alberto Barbano, Enzo Tartaglione, Claudio Berzovini +2
Early screening of patients is a critical issue in order to assess immediate and fast responses against the spread of COVID-19. The use of nasopharyngeal swabs has been considered…
Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke
Umberto A. Gava, Federico D'Agata, Enzo Tartaglione +5
Purpose: In this study we investigate whether a Convolutional Neural Network (CNN) can generate clinically relevant parametric maps from CT perfusion data in a clinical setting of…