47 citations · 138 across the 12 of their papers we have counts for
13 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…
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation
Alessandra Lumini, Loris Nanni, Gianluca Maguolo
Semantic segmentation has a wide array of applications ranging from medical-image analysis, scene understanding, autonomous driving and robotic navigation. This work deals with med…
Exploiting Adam-like Optimization Algorithms to Improve the Performance of Convolutional Neural Networks
Loris Nanni, Gianluca Maguolo, Alessandra Lumini
Stochastic gradient descent (SGD) is the main approach for training deep networks: it moves towards the optimum of the cost function by iteratively updating the parameters of a mod…
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…
Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare
Loris Nanni, Alessandra Lumini, Stefano Ghidoni +1
Classification of biological images is an important task with crucial application in many fields, such as cell phenotypes recognition, detection of cell organelles and histopatholo…
Learning morphological operators for skin detection
Alessandra Lumini, Loris Nanni, Alice Codogno +1
In this work we propose a novel post processing approach for skin detectors based on trained morphological operators. The first step, consisting in skin segmentation is performed a…
Deep learning for Plankton and Coral Classification
Alessandra Lumini, Loris Nanni, Gianluca Maguolo
Oceans are the essential lifeblood of the Earth: they provide over 70% of the oxygen and over 97% of the water. Plankton and corals are two of the most fundamental components of oc…
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…
Ensemble of Deep Learned Features for Melanoma Classification
Loris Nanni, Alessandra Lumini, Stefano Ghidoni
The aim of this work is to propose an ensemble of descriptors for Melanoma Classification, whose performance has been evaluated on validation and test datasets of the melanoma chal…
Fair comparison of skin detection approaches on publicly available datasets
Alessandra Lumini, Loris Nanni
Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to tra…