108 citations · 178 across the 9 of their papers we have counts for
9 papers
Feature transforms for image data augmentation
Loris Nanni, Michelangelo Paci, Sheryl Brahnam +1
A problem with Convolutional Neural Networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many method…
Deep ensembles in bioimage segmentation
Loris Nanni, Daniela Cuza, Alessandra Lumini +2
Semantic segmentation consists in classifying each pixel of an image by assigning it to a specific label chosen from a set of all the available ones. During the last few years, a l…
Gated recurrent units and temporal convolutional network for multilabel classification
Loris Nanni, Alessandra Lumini, Alessandro Manfe +3
Multilabel learning tackles the problem of associating a sample with multiple class labels. This work proposes a new ensemble method for managing multilabel classification: the cor…
High performing ensemble of convolutional neural networks for insect pest image detection
Loris Nanni, Alessandro Manfe, Gianluca Maguolo +2
Pest infestation is a major cause of crop damage and lost revenues worldwide. Automatic identification of invasive insects would greatly speedup the identification of pests and exp…
Comparison of different convolutional neural network activation functions and methods for building ensembles
Loris Nanni, Gianluca Maguolo, Sheryl Brahnam +1
Recently, much attention has been devoted to finding highly efficient and powerful activation functions for CNN layers. Because activation functions inject different nonlinearities…
Neural networks for Anatomical Therapeutic Chemical (ATC) classification
Loris Nanni, Alessandra Lumini, Sheryl Brahnam
Motivation: Automatic Anatomical Therapeutic Chemical (ATC) classification is a critical and highly competitive area of research in bioinformatics because of its potential for expe…
An Ensemble of Convolutional Neural Networks for Audio Classification
Loris Nanni, Gianluca Maguolo, Sheryl Brahnam +1
In this paper, ensembles of classifiers that exploit several data augmentation techniques and four signal representations for training Convolutional Neural Networks (CNNs) for audi…
iProStruct2D: Identifying protein structural classes by deep learning via 2D representations
Loris Nanni, Alessandra Lumini, Federica Pasquali +1
In this paper we address the problem of protein classification starting from a multi-view 2D representation of proteins. From each 3D protein structure, a large set of 2D projectio…
General Purpose (GenP) Bioimage Ensemble of Handcrafted and Learned Features with Data Augmentation
L. Nanni, S. Brahnam, S. Ghidoni +1
Bioimage classification plays a crucial role in many biological problems. In this work, we present a new General Purpose (GenP) ensemble that boosts performance by combining local…