Search Results for author:
Found 25 papers, 4 papers with code
Feature transforms for image data augmentation
These novel methods are based on the Fourier Transform (FT), the Radon Transform (RT) and the Discrete Cosine Transform (DCT).
Deep ensembles in bioimage segmentation
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.
Gated recurrent units and temporal convolutional network for multilabel classification
Multilabel learning tackles the problem of associating a sample with multiple class labels.
High performing ensemble of convolutional neural networks for insect pest image detection
The best performing ensemble, which combined the CNNs using the different augmentation methods and the two new Adam variants proposed here, achieved state of the art on both insect data sets: 95. 52% on Deng and 73. 46% on IP102, a score on Deng that competed with human expert classifications.
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation
The basic architecture in image segmentation consists of an encoder and a decoder: the first uses convolutional filters to extract features from the image, the second is responsible for generating the final output.
Comparison of different convolutional neural network activation functions and methods for building ensembles
Because activation functions inject different nonlinearities between layers that affect performance, varying them is one method for building robust ensembles of CNNs.
Exploiting Adam-like Optimization Algorithms to Improve the Performance of Convolutional Neural Networks
In this work, we compare Adam based variants based on the difference between the present and the past gradients, the step size is adjusted for each parameter.
Neural networks for Anatomical Therapeutic Chemical (ATC) classification
Motivation: Automatic Anatomical Therapeutic Chemical (ATC) classification is a critical and highly competitive area of research in bioinformatics because of its potential for expediting drug develop-ment and research.
Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare
In this paper we classify biomedical images using ensembles of neural networks.
Deep learning and hand-crafted features for virus image classification
The set of handcrafted is mainly based on Local Binary Pattern variants, for each descriptor a different Support Vector Machine is trained, then the set of classifiers is combined by sum rule.
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images
We assessed the impact of creating a CXR image database from different sources, and the COVID-19 generalization from one source to another.
Computed Tomography (CT)
Explainable artificial intelligence
+2
An Ensemble of Convolutional Neural Networks for Audio Classification
The best performing ensembles combining data augmentation techniques with different signal representations are compared and shown to outperform the best methods reported in the literature on these datasets.
Phasic dopamine release identification using ensemble of AlexNet
Dopamine (DA) is an organic chemical that influences several parts of behaviour and physical functions.
The computerization of archaeology: survey on AI techniques
This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: a) The use of software tools as a creative stimulus for the organization of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; b) The analysis of methods for the classification of fragments found in archaeological excavations and for the reconstruction of ceramics, with the recomposition of the parts of text missing from historical documents and epigraphs; c) The cataloguing and study of human remains to understand the social and historical context of belonging with the demonstration of the effectiveness of the AI techniques used; d) The detection of particularly difficult terrestrial archaeological sites with the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version.
A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images
In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature.
Data augmentation approaches for improving animal audio classification
To the best of our knowledge this is the largest study on data augmentation for CNNs in animal audio classification audio datasets using the same set of classifiers and parameters.
Audiogmenter: a MATLAB Toolbox for Audio Data Augmentation
Audio data augmentation is a key step in training deep neural networks for solving audio classification tasks.
Insect pest image detection and recognition based on bio-inspired methods
Our best ensembles reaches the state of the art accuracy on both the smaller dataset (92. 43%) and the IP102 dataset (61. 93%), approaching the performance of human experts on the smaller one.
Deep learning for Plankton and Coral Classification
We study how to create an ensemble based of different CNN models, fine tuned on several datasets with the aim of exploiting their diversity.
Learning morphological operators for skin detection
In this work we propose a novel post processing approach for skin detectors based on trained morphological operators.
iProStruct2D: Identifying protein structural classes by deep learning via 2D representations
In this paper we address the problem of protein classification starting from a multi-view 2D representation of proteins.
Ensemble of Convolutional Neural Networks Trained with Different Activation Functions
The goal of this work is to propose an ensemble of Convolutional Neural Networks trained using several different activation functions.
Ensemble of Deep Learned Features for Melanoma Classification
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 challenge 2018.
Fair comparison of skin detection approaches on publicly available datasets
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 track body parts and face detection.
