1,721,247 research outputs found
A critic evaluation of methods for COVID-19 automatic detection from X-ray images
No full textIn this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. We show that similar results can be obtained using X-Ray images that do not contain most of the lungs. We are able to remove the lungs from the images by turning to black the center of the X-Ray scan and training our classifiers only on the outer part of the images. Hence, we deduce that several testing protocols for the recognition are not fair and that the neural networks are learning patterns in the dataset that are not correlated to the presence of COVID-19. Finally, we show that creating a fair testing protocol is a challenging task, and we provide a method to measure how fair a specific testing protocol is. In the future research we suggest to check the fairness of a testing protocol using our tools and we encourage researchers to look for better techniques than the ones that we propose
Set of Approaches Based on Position Specific Scoring Matrix and Amino Acid Sequence for Primary Category Enzyme Classification
No full textDalla calce della Fornace Bianchi ai dipinti di Gino Grimaldi. Conservazione integrata, sostenibile e partecipata a Cogoleto dal 2007 al 2016
No full textIn un contesto in cui le risorse economiche pubbliche e private sono poche, come si possono perseguire fruttosi interventi di recupero e valorizzazione? In anni di recessione e crisi quali quelli attuali si possono portare avanti iniziative di qualità? Secondo gli autori questo è possibile: il libro ripercorre ciò che è stato compiuto nel comune di Cogoleto tra il 2008 ed il 2015 descrivendo non solo i risultati raggiunti ma anche tutto il processo percorso.
L’elemento vincente è stato una forte sinergia tra il Comune, l’Università (DSA-Scuola Politecnica di Genova), gli enti di tutela (Soprintendenza ai Beni Architettonici e ai Beni Artistici), i laboratori di ricerca (ICVBC del CNR di Firenze), le istituzioni locali (ESSEG, Consulta Giovani Cogoleto,CSM-ASL3), gli Ordini professionali (OAPPCG), i finanziatori privati (Compagnia di San Paolo di Torino) e le molte organizzazioni religiose e di volontariato (ISCUM, FBC, ACCO, Cogoleto Live, A.C.GinoGrimaldi, A.C.Pratozanino, A.C.MarcoRossi , UNItré Arenzano Cogoleto, Parrocchia S.M.Maggiore, Confraternita S.Lorenzo, Gruppo AGESCI). Questa rete di collaborazioni e questo approccio hanno consentito di realizzare interventi quali il restauro archeologico della Fornace Bianchi, l’istituzione dell’Area di Archeologia Industriale Tiziano Mannoni e gli studi e la messa in sicurezza dei dipinti di Gino Grimaldi nella chiesa di Santa Maria Addolorata nell’Ospedale Psichiatrico di Pratozanino.
Gli aspetti di comunicazione sono stati importanti in questo processo, con le conferenze, i laboratori realizzati per il Festival della Scienza di Genova dal 2012 al 2015, gli spettacoli teatrali ed i vari siti web delle associazioni coinvolte. Questi canali hanno svolto un ruolo polivalente di divulgazione al pubblico, approfondimento e stimolo per ulteriori iniziative
Geosigmeti, unità di paesaggio e reti ecologiche. In: Carlo Blasi, Adriano Paolella (a cura) “Identificazione e cambiamenti nel paesaggio contemporaneo” Atti del Terzo Congrasso IAED (Roma, 4/6 dicembre 2003)
No full textNeural networks for anatomical therapeutic chemical (ATC) classification
Full text linkPurpose Automatic anatomical therapeutic chemical (ATC) classification is progressing at a rapid pace because of its potential in drug development. Predicting an unknown compound's therapeutic and chemical characteristics in terms of how it affects multiple organs and physiological systems makes automatic ATC classification a vital yet challenging multilabel problem. The aim of this paper is to experimentally derive an ensemble of different feature descriptors and classifiers for ATC classification that outperforms the state-of-the-art. Design/methodology/approach The proposed method is an ensemble generated by the fusion of neural networks (i.e. a tabular model and long short-term memory networks (LSTM)) and multilabel classifiers based on multiple linear regression (hMuLab). All classifiers are trained on three sets of descriptors. Features extracted from the trained LSTMs are also fed into hMuLab. Evaluations of ensembles are compared on a benchmark data set of 3883 ATC-coded pharmaceuticals taken from KEGG, a publicly available drug databank. Findings Experiments demonstrate the power of the authors’ best ensemble, EnsATC, which is shown to outperform the best methods reported in the literature, including the state-of-the-art developed by the fast.ai research group. The MATLAB source code of the authors’ system is freely available to the public at https://github.com/LorisNanni/Neural-networks-for-anatomical-therapeutic-chemical-ATC-classification. Originality/value This study demonstrates the power of extracting LSTM features and combining them with ATC descriptors in ensembles for ATC classification
Deep features for training support vector machines
Full text linkFeatures play a crucial role in computer vision. Initially designed to detect salient elements by means of handcrafted algorithms, features now are often learned using different layers in convo-lutional neural networks (CNNs). This paper develops a generic computer vision system based on features extracted from trained CNNs. Multiple learned features are combined into a single structure to work on different image classification tasks. The proposed system was derived by testing several approaches for extracting features from the inner layers of CNNs and using them as inputs to support vector machines that are then combined by sum rule. Several dimensionality reduction techniques were tested for reducing the high dimensionality of the inner layers so that they can work with SVMs. The empirically derived generic vision system based on applying a discrete cosine transform (DCT) separately to each channel is shown to significantly boost the performance of standard CNNs across a large and diverse collection of image data sets. In addition, an ensemble of different topologies taking the same DCT approach and combined with global mean thresholding pooling obtained state-of-the-art results on a benchmark image virus data set
An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images
No full textThe analysis of road continuity in satellite images is a complex challenge. This is due to the difficulty in identifying the directional vector of road sections, especially when the satellite view of roads is obstructed by trees or other structures. Today, most research focuses on optimizing the deep learning network topology, however, the accuracy of segmentation is affected by the loss function used in training; currently, little research has been published on ad-hoc loss functions for road segmentation. To solve this problem, we proposed loss functions based on topological pixel analysis, in which more weight is given to problematic pixels representing non-real road breaks. We report the results of different tests, obtaining state-of-the-art performance among convolution neural network-based approaches. For instance, on the Massachusetts Roads dataset, our method achieved a Dice score of 75.34% and an IoU of 60.44%, compared to the best baseline scores of 74.64% and 59.51% achieved by GapLoss. Similarly, on the DeepGlobe Roads dataset, our method obtained a Dice score of 79.78% and an IoU of 66.36%, outperforming the best baseline scores of 78.62% and 64.47% by GapLoss. Both the code and information for replicating our experiments are available at https://github.com/LorisNanni/An-Enhanced-Loss-Function-for-Semantic-Road-Segmentation-in-Remote-Sensing-Images, so as to enable future reliable comparisons
Ensemble of convolutional neural networks trained with different activation functions
Full text linkActivation functions play a vital role in the training of Convolutional Neural Networks. For this reason, developing efficient and well-performing functions is a crucial problem in the deep learning community. The idea of these approaches is to allow a reliable parameter learning, avoiding vanishing gradient problems. The goal of this work is to propose an ensemble of Convolutional Neural Networks trained using several different activation functions. Moreover, a novel activation function is here proposed for the first time. Our aim is to improve the performance of Convolutional Neural Networks in small/medium sized biomedical datasets. Our results clearly show that the proposed ensemble outperforms Convolutional Neural Networks trained with a standard ReLU as activation function. The proposed ensemble outperforms with a p-value of 0.01 each tested stand-alone activation function; for reliable performance comparison we tested our approach on more than 10 datasets, using two well-known Convolutional Neural Networks: Vgg16 and ResNet50
Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation
Full text linkTo identify objects in images, a complex set of skills is needed that includes understanding the context and being able to determine the borders of objects. In computer vision, this task is known as semantic segmentation and it involves categorizing each pixel in an image. It is crucial in many real-world situations: for autonomous vehicles, it enables the identification of objects in the surrounding area; in medical diagnosis, it enhances the ability to detect dangerous pathologies early, thereby reducing the risk of serious consequences. In this study, we compare the performance of various ensembles of convolutional and transformer neural networks. Ensembles can be created, e.g., by varying the loss function, the data augmentation method, or the learning rate strategy. Our proposed ensemble, which uses a simple averaging rule, demonstrates exceptional performance across multiple datasets. Notably, compared to prior state-of-the-art methods, our ensemble consistently shows improvements in the well-studied polyp segmentation problem. This problem involves the precise delineation and identification of polyps within medical images, and our approach showcases noteworthy advancements in this domain, obtaining an average Dice of 0.887, which outperforms the current SOTA with an average Dice of 0.885
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