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1.
Sci Rep ; 10(1): 16476, 2020 10 05.
Artigo em Inglês | MEDLINE | ID: mdl-33020567

RESUMO

After the COVID-19 worldwide spread, evidence suggested a vast diffusion of negative consequences on people's mental health. Together with depression and sleep difficulties, anxiety symptoms seem to be the most diffused clinical outcome. The current contribution aimed to examine attentional bias for virus-related stimuli in people varying in their degree of health anxiety (HA). Consistent with previous literature, it was hypothesized that higher HA would predict attentional bias, tested using a visual dot-probe task, to virus-related stimuli. Participants were 132 Italian individuals that participated in the study during the lockdown phase in Italy. Results indicated that the HA level predicts attentional bias toward virus-related objects. This relationship is double mediated by the belief of contagion and by the consequences of contagion as assessed through a recent questionnaire developed to measure the fear for COVID-19. These findings are discussed in the context of cognitive-behavioral conceptualizations of anxiety suggesting a risk for a loop effect. Future research directions are outlined.


Assuntos
Ansiedade/epidemiologia , Atenção , Infecções por Coronavirus/psicologia , Pneumonia Viral/psicologia , Adulto , Ansiedade/psicologia , Infecções por Coronavirus/epidemiologia , Medo , Feminino , Humanos , Itália , Masculino , Pandemias , Pneumonia Viral/epidemiologia , Testes Psicológicos/estatística & dados numéricos
2.
Artigo em Russo | MEDLINE | ID: mdl-33081450

RESUMO

BACKGROUND: The assessment of pedophilic disorder is one of the most complicated problems in forensic psychiatric practice. OBJECTIVE: To evaluate the use of eye tracking to identify pedophilic disorder. MATERIAL AND METHODS: One hundred people were stratified into a group of men with pedophilic disorder (n=43) and a group of people without clinical signs of pedophilia (n=57). Clinical, psychopathological, sexological and psychophysiological methods were used. Within the framework of a psychophysiological method, normative and deviant erotic stimuli were presented with simultaneous eye gaze recording. RESULTS AND CONCLUSION: Methodological requirements to create the diagnostic visual tests, in the development of which the stimulus material should be highly standardized and take into account the color spectrum, size, content, as well as the emotional richness of the images, were suggested. Moreover, it was found that for the detection of paraphilic interest, it was very important to assess attention (involuntary and voluntary), based on the study of fixation activity: high rates of the total stimulus viewing time, duration of the first fixation, average duration of fixations and index of fixation activity indicate a high level of attention to the exposed deviant stimuli in subjects with pedophilic disorder, which shows their greater importance in comparison with normative stimuli. However, the individual analysis of the data should take into account not only individual physiological indicators, but also their combination, since an oculomotor activity is significantly influenced by internal and external factors. The obtained data suggest that eye tracking is a very promising diagnostic method to identify paraphilic disorders.


Assuntos
Pedofilia , Atenção , Emoções , Movimentos Oculares , Humanos , Masculino , Pedofilia/diagnóstico
3.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3779-3782, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018824

RESUMO

In this paper, a perception-empathy biofeedback (PEBF) system is proposed that supplements the foot pressure status of a paralyzed foot with a wearable vibrotactile biofeedback (BF) vest to the back. Improvements in the ankle dorsiflexion and push-off movement in the swing phase and pre-swing phase, respectively, can be expected after using the proposed system. However, the results of the 3 week pilot clinical tests suggest that significant improvement is only observed for the push-off movement. It is assumed that the attention required to recognize the BF was beyond the ability of the patients. In this paper, a dual task (40 s walking and performing mental arithmetic at the same time) was conducted with the following conditions: no vibrations and providing BF to the lower back and the entire back. According to the results, the ankle joint angle of the paralyzed side at push-off under the entire back condition is statistically significant (p = 0.0780); however, there are no significant changes under the lower back condition (p = 0.4998). Moreover, the ankle joint angle of the paralyzed side at the initial contact is statistically significant with respect to the lower back condition (p = 0.0233) and shows a significant trend for the entire back condition (p = 0.0730). The results suggest that the limited attention capacity of hemiplegic patients fails to improve both dorsiflexion and push-off movements; moreover, ankle motion can be promoted if attention is concentrated on recognizing focalized vibratory feedback patterns.


Assuntos
Empatia , Vibração , Atenção , Biorretroalimentação Psicológica , Humanos , Caminhada
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5232-5235, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019164

RESUMO

Students, office workers, or other computer and mobile device users can suffer from decrements in alertness or productivity, but many intervention methods on these can be too distracting or even affect daily routines. Using heart rate (HR) to determine a fast and slow target frequency at which to oscillate light brightness stimulation on a laptop, thirty-six participants joined a cognitive task where we hypothesized that fast frequency stimulation would increase alertness and decrease relaxation, while slow frequency stimulation would have the opposite effects. We found that slow frequency stimulation produces a statistically significant delay in response time, users react more slowly (3.8e2 ± 5.5e1 ms), when compared to the no stimulation (3.7e2 ± 4.1e1 ms) (p = 9.0e-3) conditions. The (Slow - No Stimulation) response time (1.7e1 ± 2.7e2 ms) produced a statistically significant delay in response time versus the (Fast - No Stimulation) response time (-0.74 ± 2.4e1 ms) (p = .016). These delays due to slow stimulation occurred without influencing accuracy or subjective sleepiness ratings. We observed that frequency-dependent light stimulation can potentially influence HRV metrics such as the mean normal-to-normal intervals and mean HR. Future work will target breathing rate to determine light stimulation oscillations as we further investigate the potential of using the slow-frequency domain to unobtrusively influence user performance and physiology.


Assuntos
Atenção , Vigília , Frequência Cardíaca , Humanos , Microcomputadores , Tempo de Reação
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5280-5283, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019175

RESUMO

Depression is a harmful disease with high incidence. However, no effective method based on physiological information detection has been published to diagnose depression. Electroencephalography (EEG) has been used as a tool to detect physiological information of depressed patients and the symmetry of EEG receives much attention. This research focused on the symmetry of EEG in left and right homologous brain regions. 22 healthy volunteers and 41 volunteers of major depression were tested and three methods, average power ratio, waveform correlation and power spectral correlation, were adopted to measure the symmetry in all frequency bands and all brain regions. After t-test, homologous site pairs in particular frequency bands with significant differences between major depressed patients and controls were found out. Then sample entropy analysis was adopted, trying to figure out further connections between EEG symmetry and major depression. The accuracy tests were also taken and the average accuracy of some tests could reach 93.7%. The result of this research can hopefully serve as a theoretical basis for pattern recognition in the diagnosis of depression. The accuracy of pattern recognition based on multiple processing methods and sites will increase dramatically.


Assuntos
Transtorno Depressivo Maior , Atenção , Encéfalo , Transtorno Depressivo Maior/diagnóstico , Eletroencefalografia , Entropia , Humanos
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5472-5475, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019218

RESUMO

Automated diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) from brain's functional imaging has gained more interest due to its high prevalence rates among children. While phenotypic information, such as age and gender, is known to be important in diagnosing ADHD and critically affects the representation derived from fMRI brain images, limited studies have integrated phenotypic information when learning discriminative embedding from brain imaging for such an automatic classification task. In this work, we propose to integrate age and gender attributes through attention mechanism that is jointly optimized when learning a brain connectivity embedding using convolutional variational autoencoder derived from resting state functional magnetic resonance imaging (rs-fMRI) data. Our proposed framework achieves a state-of-the-art average of 86.22% accuracy in ADHD vs. typical develop control (TDC) binary classification task evaluated across five public ADHD-200 competition datasets. Furthermore, our analysis points out that there are insufficient linked connections to the brain region of precuneus in the ADHD group.


Assuntos
Transtorno do Deficit de Atenção com Hiperatividade , Imagem por Ressonância Magnética , Atenção , Transtorno do Deficit de Atenção com Hiperatividade/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Criança , Humanos
7.
Artigo em Inglês | MEDLINE | ID: mdl-33017932

RESUMO

This study analyzed the selective attention processing related to cognitive load on simultaneous interpretation (SI). We tested simultaneous interpreter's brain function using EEG signals and calculated inter-trial coherence (ITC) extracted by the 40-Hz auditory steady-state response (ASSR). In this experiment, we set two conditions as Japanese-English translation and Japanese shadowing cognition. We also compared two subject groups: S rank with more than 15 years of SI experience (n=7) and C rank with less than one year experience (n=15). As a result, the ITCs for S rank in interpreting conditions were more significantly increased than C rank in the shadowing conditions (ITC: p<0.001). Our results demonstrate that 40-Hz ASSR might be a good indicator of selective attention and cognitive load during SI in ecologically valid environmental conditions. It can also be used to detect attention and cognitive control dysfunction in ADHD or schizophrenia.


Assuntos
Eletroencefalografia , Potenciais Evocados Auditivos , Estimulação Acústica , Atenção , Sincronização de Fases em Eletroencefalografia
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 128-133, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017947

RESUMO

Electroencephalography (EEG)-based depression detection has become a hot topic in the development of biomedical engineering. However, the complexity and nonstationarity of EEG signals are two biggest obstacles to this application. In addition, the generalization of detection algorithms may be degraded owing to the influences brought by individual differences. In view of the correlation between EEG signals and individual demographics, such as gender, age, etc., and influences of these demographic factors on the incidence of depression, it would be better to incorporate demographic factors during EEG modeling and depression detection. In this work, we constructed an one-dimensional Convolutional Neural Network (1-D CNN) to obtain more effective features of EEG signals, then integrated gender and age factors into the 1-D CNN via an attention mechanism, which could prompt our 1-D CNN to explore complex correlations between EEG signals and demographic factors, and generate more effective high-level representations ultimately for the detection of depression. Experimental results on 170 (81 depressed patients and 89 normal controls) subjects showed that the proposed method is superior to the unitary 1-D CNN without gender and age factors and two other ways of incorporating demographics. This work also indicates that organic mixture of EEG signals and demographic factors is promising for the detection of depression.Clinical relevance-This work indicates that organically mixture of EEG signals and demographic factors is promising for the detection of depression.


Assuntos
Depressão , Redes Neurais de Computação , Atenção , Demografia , Depressão/diagnóstico , Eletroencefalografia , Humanos
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 146-149, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017951

RESUMO

The aim of this study was to design a new deep learning framework for end-to-end processing of polysomnograms. This framework can be trained to analyze whole-night polysomnograms without the limitations of and bias towards clinical scoring guidelines. We validated the framework by predicting the age of subjects. We designed a hierarchical attention network architecture, which can be pre-trained to predict labels based on 5-minute epochs of data and fine-tuned to predict based on whole-night polysomnography recordings. The model was trained on 511 recordings from the Cleveland Family study and tested on 146 test subjects aged between 6 to 88 years. The proposed network achieved a mean absolute error of 7.36 years and a correlation to true age of 0.857. Sleep can be analyzed using our end-to-end deep learning framework, which we expect can generalize to learning other subject-specific labels such as sleep disorders. The difference in the predicted and chronological age is further proposed as an estimate of biological age.


Assuntos
Aprendizado Profundo , Redes Neurais de Computação , Atenção , Polissonografia , Sono
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 172-175, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017957

RESUMO

In this paper, the classification problem of schizophrenia patients from healthy controls is considered, whose goal is to explore the relationship between DNA characteristics and schizophrenia. However, the DNA methylation data has the properties of small samples in high dimension and non-Gaussian distribution which makes it hard to do classification with DNA methylation data. Hence a classification method based on deep learning is designed. We propose a feature selection method based on attention mechanism which embeds a weight gated layer in the network structure to get a task-related sparse representation of the DNA methylation data. The performance of proposed method outperforms existing feature selection methods. On a real-world data set, the classification with proposed method achieves a high accuracy.


Assuntos
Metilação de DNA , Esquizofrenia , Atenção , Aprendizado Profundo , Humanos , Esquizofrenia/genética
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 184-187, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017960

RESUMO

Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals. For analysis of physiological recordings, models based on temporal convolutional networks and recurrent neural networks have demonstrated encouraging results and an ability to capture complex patterns and dependencies in the data. However, representations that capture the entirety of the raw signal are suboptimal as not all portions of the signal are equally important. As such, attention mechanisms are proposed to divert focus to regions of interest, reducing computational cost and enhancing accuracy. Here, we evaluate attention-based frameworks for the classification of physiological signals in different clinical domains. We evaluated our methodology on three classification scenarios: neurogenerative disorders, neurological status and seizure type. We demonstrate that attention networks can outperform traditional deep learning models for sequence modelling by identifying the most relevant attributes of an input signal for decision making. This work highlights the benefits of attention-based models for analysing raw data in the field of biomedical research.


Assuntos
Atenção , Redes Neurais de Computação , Bases de Dados Genéticas , Humanos , Convulsões
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 268-271, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017980

RESUMO

We propose a method with attention-based recurrent neural networks (ARNN) for detecting the semantic incongruities in spoken sentences using single-trial electroencephalogram (EEG) signals. 19 participants listened to sentences, some of which included semantically anomalous words. We recorded their EEG signals while they listened. Although previous detection approaches used a word's explicit onset, we used the EEG signals of the whole regions of each sentence, which made it possible to classify the correctness of the sentences without the onset information of the anomalous words. ARNN achieved 63.5% classification accuracy with a statistical significance above the chance level and also above the performances which includes onset information (50.9%). Our results also demonstrated that the attention weights of the model showed that the predictions depended on the feature vectors that are temporally close to the onsets of the anomalous words.


Assuntos
Percepção da Fala , Fala , Atenção , Eletroencefalografia , Humanos , Semântica
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 418-421, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018017

RESUMO

The multi-label electrocardiogram (ECG) classification is to automatically predict a set of concurrent cardiac abnormalities in an ECG record, which is significant for clinical diagnosis. Modeling the cardiac abnormality dependencies is the key to improving classification performance. To capture the dependencies, we proposed a multi-label classification method based on the weighted graph attention networks. In the study, a graph taking each class as a node was mapped and the class dependencies were represented by the weights of graph edges. A novel weights generation method was proposed by combining the self-attentional weights and the prior learned co-occurrence knowledge of classes. The algorithm was evaluated on the dataset of the Hefei Hi-tech Cup ECG Intelligent Competition for 34 kinds of ECG abnormalities classification. And the micro-f 1 and the macro-f 1 of cross validation respectively were 91.45% and 44.48%. The experiment results show that the proposed method can model class dependencies and improve classification performance.


Assuntos
Arritmias Cardíacas , Eletrocardiografia , Algoritmos , Atenção , Humanos , Projetos de Pesquisa
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 754-759, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018096

RESUMO

This paper focuses on the use of an attention-based encoder-decoder model for the task of breathing sound segmentation and detection. This study aims to accurately segment the inspiration and expiration of patients with pulmonary diseases using the proposed model. Spectrograms of the lung sound signals and labels for every time segment were used to train the model. The model would first encode the spectrogram and then detect inspiratory or expiratory sounds using the encoded image on an attention-based decoder. Physicians would be able to make a more precise diagnosis based on the more interpretable outputs with the assistance of the attention mechanism.The respiratory sounds used for training and testing were recorded from 22 participants using digital stethoscopes or anti-noising microphone sets. Experimental results showed a high 92.006% accuracy when applied 0.5 second time segments and ResNet101 as encoder. Consistent performance of the proposed method can be observed from ten-fold cross-validation experiments.


Assuntos
Respiração , Sons Respiratórios , Atenção , Expiração , Humanos , Aprendizado de Máquina
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1019-1022, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018158

RESUMO

The N2pc event-related potential component measures direction and time course of selective visual attention and represents an important biomarker in cognitive neuroscience. While its subtractive origin strongly influences the amplitude, thus hindering its detection, other external factors, such as subject's inefficiency to allocate attention to the cued target, or the heterogeneity of the visual context, may strongly affect the elicitation of the component itself. It would therefore be extremely important to create a tool that, using as few sweeps as possible, could reliably establish whether an N2pc is present in an individual subject. In the present work, we propose an approach by resorting to a time-frequency analysis of N2pc individual signals; in particular, power at each frequency band (α/ß/δ/θ) was computed in the N2 time range and correlated to the estimated amplitude of the N2pc. Preliminary results on fourteen human volunteers of a visual search design showed a very high correlation coefficient (over 0.9) between the low frequency bands power and the mean absolute amplitude of the component, using only 40 sweeps. Results also seemed to suggest that N2pc amplitude values higher than 0.5 µV could be accurately classified according to time-frequency indices.Clinical Relevance - The online detection of the N2pc presence in individual EEG datasets would allow not only to study the factors responsible of N2pc variability across subjects and conditions, but also to investigate novel search variants on participants with a predisposition to show an N2pc, reducing time and costs and the possibility to obtain biased results.


Assuntos
Neurociência Cognitiva , Eletroencefalografia , Atenção , Sinais (Psicologia) , Potenciais Evocados , Humanos
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1071-1074, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018171

RESUMO

While Deep Learning methods have been successfully applied to tackle a wide variety of prediction problems, their application has been mostly limited to data structured in a grid-like fashion. However, the study of the human brain "connectome" involves the representation of the brain as a graph with interacting nodes. In this paper, we extend the Graph Attention Network (GAT), a novel neural network (NN) architecture acting on the features of the nodes of a binary graph, to handle a set of graphs provided with node features and non-binary edge weights. We demonstrate the effectiveness of our architecture by training it multimodal data collected from a large homogeneous fMRI dataset (n=1003 individuals with multiple fMRI sessions per subject) made publicly available by the Human Connectome Project (HCP), demonstrating good performance and seamless integration of multimodal neuroimaging data. Our adaptation provides a powerful and flexible deep learning tool to integrate multimodal neuroimaging connectomics data in a predictive context.


Assuntos
Encéfalo , Conectoma , Atenção , Encéfalo/diagnóstico por imagem , Humanos , Imagem por Ressonância Magnética , Neuroimagem
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1207-1210, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018204

RESUMO

Accurate segmentation of pulmonary vein (PV) and left atrium (LA) is essential for the preoperative evaluation and planning of total anomalous pulmonary venous connection (TAPVC), which is a rare but mortal congenital heart disease of children. However, manual segmentation is time-consuming and insipid. To free radiologists from the repetitive work, we propose an automatic deep learning method to segment PV and LA from Low-Dose CT images. In the method, attention mechanism is incorporated into the widely used V-Net and a novel grouped attention module is applied to enforce the segmentation performance of the V-Net. We evaluate our method on 68 3D Low-Dose CT images scanned from patients with TAPVC. The experiment result shows that our method outperforms the popular 3D-UNet and V-Net, with mean dice similarity coefficient (DSC) of 0.795 and 0.834 for the PV and LA respectively.Clinical relevance-We proposed a CNNs-based method for the automatic segmentation of PV and LA with good accuracy, which can be used for the preoperative evaluation and planning of TAPVC. Our method can improve the efficiency and reduce the workloads of radiologists (400 milliseconds vs. 2-3 hours per-case).


Assuntos
Veias Pulmonares , Síndrome de Cimitarra , Atenção , Criança , Átrios do Coração/diagnóstico por imagem , Humanos , Imageamento Tridimensional , Veias Pulmonares/diagnóstico por imagem
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1258-1261, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018216

RESUMO

Despite the potential of deep convolutional neural networks for classification of thorax diseases from chest X-ray images, this task is still challenging as it is categorized as a weakly supervised learning problem, and deep neural networks in general suffer from a lack of interpretability. In this paper, a deep convolutional neural network framework with recurrent attention mechanism was investigated to annotate abnormalities in chest X-ray images. A modified MobileNet architecture was adapted in the framework for classification and the prediction difference analysis method was utilized to visualize the basis of network's decision on each image. A long short-term memory network was utilized as the attention model to focus on relevant regions of each image for classification. The framework was evaluated on NIH chest X-ray dataset. The attention-guided model versus the model with no attention mechanism could annotate the images in an independent test set with an F1-score of 0.58 versus 0.46, and an AUC of 0.94 versus 0.73. The obtained results implied that the proposed attention-guided model could outperform the other methods investigated previously for annotating the same dataset.


Assuntos
Algoritmos , Redes Neurais de Computação , Atenção , Tórax , Raios X
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1592-1595, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018298

RESUMO

Clinically, the Fundus Fluorescein Angiography (FA) is a more common mean for Diabetic Retinopathy (DR) detection since the DR appears in FA much more contrasty than in Color Fundus Image (CF). However, acquiring FA has a risk of death due to the fluorescent allergy. Thus, in this paper, we explore a novel unpaired CycleGAN-based model for the FA synthesis from CF, where some strict structure similarity constraints are employed to guarantee the perfectly mapping from one domain to another one. First, a triple multi-scale network architecture with multi-scale inputs, multi-scale discriminators and multi-scale cycle consistency losses is proposed to enhance the similarity between two retinal modalities from different scales. Second, the self-attention mechanism is introduced to improve the adaptive domain mapping ability of the model. Third, to further improve strict constraints in the feather level, quality loss is employed between each process of generation and reconstruction. Qualitative examples, as well as quantitative evaluation, are provided to support the robustness and the accuracy of our proposed method.


Assuntos
Retinopatia Diabética , Retina , Atenção , Retinopatia Diabética/diagnóstico , Angiofluoresceinografia , Fundo de Olho , Humanos , Retina/diagnóstico por imagem
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1616-1619, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018304

RESUMO

Semantic segmentation is a fundamental and challenging problem in medical image analysis. At present, deep convolutional neural network plays a dominant role in medical image segmentation. The existing problems of this field are making less use of image information and learning few edge features, which may lead to the ambiguous boundary and inhomogeneous intensity distribution of the result. Since the characteristics of different stages are highly inconsistent, these two cannot be directly combined. In this paper, we proposed the Attention and Edge Constraint Network (AEC-Net) to optimize features by introducing attention mechanisms in the lower-level features, so that it can be better combined with higher-level features. Meanwhile, an edge branch is added to the network which can learn edge and texture features simultaneously. We evaluated this model on three datasets, including skin cancer segmentation, vessel segmentation, and lung segmentation. Results demonstrate that the proposed model has achieved state-of-the-art performance on all datasets.


Assuntos
Processamento de Imagem Assistida por Computador , Neoplasias Cutâneas , Atenção , Humanos , Pulmão/diagnóstico por imagem , Redes Neurais de Computação
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