Search Results for author: Giancarlo Mauri

Found 7 papers, 0 papers with code

Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection

no code implementations31 May 2019 Changhee Han, Leonardo Rundo, Ryosuke Araki, Yudai Nagano, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi

In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; researchers have improved classification by augmenting data with noise-to-image (e. g., random noise samples to diverse pathological images) or image-to-image GANs (e. g., a benign image to a malignant one).

General Classification Image Augmentation +2

USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

no code implementations17 Apr 2019 Leonardo Rundo, Changhee Han, Yudai Nagano, Jin Zhang, Ryuichiro Hataya, Carmelo Militello, Andrea Tangherloni, Marco S. Nobile, Claudio Ferretti, Daniela Besozzi, Maria Carla Gilardi, Salvatore Vitabile, Giancarlo Mauri, Hideki Nakayama, Paolo Cazzaniga

The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations.

Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection

no code implementations29 Mar 2019 Changhee Han, Leonardo Rundo, Ryosuke Araki, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi

Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transformations of original images; however, those transformed images intrinsically have a similar distribution to the original ones, leading to limited performance improvement.

Data Augmentation

Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models

no code implementations8 Mar 2017 Daniele Ramazzotti, Marco S. Nobile, Paolo Cazzaniga, Giancarlo Mauri, Marco Antoniotti

The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with a functional selective advantage.

Global Optimization

A Hybrid Monte Carlo Ant Colony Optimization Approach for Protein Structure Prediction in the HP Model

no code implementations30 Sep 2013 Andrea G. Citrolo, Giancarlo Mauri

The hydrophobic-polar (HP) model has been widely studied in the field of protein structure prediction (PSP) both for theoretical purposes and as a benchmark for new optimization strategies.

Protein Structure Prediction

Proceedings Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation

no code implementations27 Sep 2013 Alex Graudenzi, Giulio Caravagna, Giancarlo Mauri, Marco Antoniotti

The Wivace 2013 Electronic Proceedings in Theoretical Computer Science (EPTCS) contain some selected long and short articles accepted for the presentation at Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation, which was held at the University of Milan-Bicocca, Milan, on the 1st and 2nd of July, 2013.

Artificial Life

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