no code implementations • 24 Apr 2024 • Alberto Presta, Gabriele Spadaro, Enzo Tartaglione, Attilio Fiandrotti, Marco Grangetto
In Learned Image Compression (LIC), a model is trained at encoding and decoding images sampled from a source domain, often outperforming traditional codecs on natural images; yet its performance may be far from optimal on images sampled from different domains.
no code implementations • 27 Mar 2024 • Guglielmo Gallone, Francesco Iodice, Alberto Presta, Davide Tore, Ovidio de Filippo, Michele Visciano, Carlo Alberto Barbano, Alessandro Serafini, Paola Gorrini, Alessandro Bruno, Walter Grosso Marra, James Hughes, Mario Iannaccone, Paolo Fonio, Attilio Fiandrotti, Alessandro Depaoli, Marco Grangetto, Gaetano Maria de Ferrari, Fabrizio D'Ascenzo
A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58. 4% male, median age 63 [51-74] years) with available paired chest x-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months.
no code implementations • 19 Jan 2024 • Théophile Rageau, Laurence Likforman-Sulem, Attilio Fiandrotti, Victoria Eyharabide, Béatrice Caseau, Jean-Claude Cheynet
A first deep convolutional neural network (CNN) detects characters in the seal (character localization).
1 code implementation • 19 Dec 2023 • Carl De Sousa Trias, Mihai Petru Mitrea, Attilio Fiandrotti, Marco Cagnazzo, Sumanta Chaudhuri, Enzo Tartaglione
We advance a method to re-synchronize the order of permuted neurons.
no code implementations • 1 Aug 2022 • Hafiza Ayesha Hoor Chaudhry, Riccardo Renzulli, Daniele Perlo, Francesca Santinelli, Stefano Tibaldi, Carmen Cristiano, Marco Grosso, Attilio Fiandrotti, Maurizio Lucenteforte, Davide Cavagnino
The accurate and consistent border segmentation plays an important role in the tumor volume estimation and its treatment in the field of Medical Image Segmentation.
no code implementations • 12 Jul 2021 • Enzo Tartaglione, Stéphane Lathuilière, Attilio Fiandrotti, Marco Cagnazzo, Marco Grangetto
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 gradient descent.
1 code implementation • 7 Feb 2021 • Enzo Tartaglione, Andrea Bragagnolo, Francesco Odierna, Attilio Fiandrotti, Marco Grangetto
Deep neural networks include millions of learnable parameters, making their deployment over resource-constrained devices problematic.
1 code implementation • 25 Jan 2021 • Carlo Alberto Barbano, Daniele Perlo, Enzo Tartaglione, Attilio Fiandrotti, Luca Bertero, Paola Cassoni, Marco Grangetto
Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma.
Ranked #1 on Colorectal Polyps Characterization on UNITOPATHO
Colorectal Polyps Characterization General Classification +3
no code implementations • 16 Nov 2020 • Enzo Tartaglione, Andrea Bragagnolo, Attilio Fiandrotti, Marco Grangetto
LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training neural networks having a sparse topology.
no code implementations • 21 Sep 2020 • Marcelin Tworski, Stéphane Lathuilière, Salim Belkarfa, Attilio Fiandrotti, Marco Cagnazzo
In this work, we tackle the problem of estimating a camera capability to preserve fine texture details at a given lighting condition.
no code implementations • 11 Jan 2019 • Andrea Migliorati, Attilio Fiandrotti, Gianluca Francini, Skjalg Lepsoy, Riccardo Leonardi
We propose a convolutional network framework for learning binary patch descriptors where pixel domain features are fused with features extracted from the transformed domain.
no code implementations • NeurIPS 2018 • Enzo Tartaglione, Skjalg Lepsøy, Attilio Fiandrotti, Gianluca Francini
The ever-increasing number of parameters in deep neural networks poses challenges for memory-limited applications.
no code implementations • 7 Jul 2017 • Attilio Fiandrotti, Sophie M. Fosson, Chiara Ravazzi, Enrico Magli
Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain.