A Temporal Neural Trace of Wavelet Coefficients in Human Object Vision: An MEG Study

Hatamimajoumerd, Elaheh; Talebpour, Alireza

doi:10.3389/fncir.2019.00020

Cited by 10 publications

(6 citation statements)

References 38 publications

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“…For 1D input data as the deep radiomics features mined in the earlier phases, the DWT procedure is accomplished through convolving the input features with a low and high pass filter [ 52 ]. After that, a reduction process is accomplished by downsampling the output data by 2 [ 53 ]. Subsequently, two clusters of coefficients are produced called the approximation coefficients CA 1 and detail coefficients CD 1 [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

Attallah

Sharkas

2021

Contrast Media & Molecular Imaging

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The rates of skin cancer (SC) are rising every year and becoming a critical health issue worldwide. SC’s early and accurate diagnosis is the key procedure to reduce these rates and improve survivability. However, the manual diagnosis is exhausting, complicated, expensive, prone to diagnostic error, and highly dependent on the dermatologist’s experience and abilities. Thus, there is a vital need to create automated dermatologist tools that are capable of accurately classifying SC subclasses. Recently, artificial intelligence (AI) techniques including machine learning (ML) and deep learning (DL) have verified the success of computer-assisted dermatologist tools in the automatic diagnosis and detection of SC diseases. Previous AI-based dermatologist tools are based on features which are either high-level features based on DL methods or low-level features based on handcrafted operations. Most of them were constructed for binary classification of SC. This study proposes an intelligent dermatologist tool to accurately diagnose multiple skin lesions automatically. This tool incorporates manifold radiomics features categories involving high-level features such as ResNet-50, DenseNet-201, and DarkNet-53 and low-level features including discrete wavelet transform (DWT) and local binary pattern (LBP). The results of the proposed intelligent tool prove that merging manifold features of different categories has a high influence on the classification accuracy. Moreover, these results are superior to those obtained by other related AI-based dermatologist tools. Therefore, the proposed intelligent tool can be used by dermatologists to help them in the accurate diagnosis of the SC subcategory. It can also overcome manual diagnosis limitations, reduce the rates of infection, and enhance survival rates.

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Section: Methodsmentioning

confidence: 99%

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

Attallah

Sharkas

2021

Contrast Media & Molecular Imaging

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“…In the case of 1-D data, like the deep spatial features mined from the 10 CNNs utilized in CoMB-Deep, the DWT process is performed by passing the data through low and high pass filters (Demirel et al, 2009). Afterward, a downsampling step is accomplished to reduce the data dimension (Hatamimajoumerd and Talebpour, 2019). Two sets of coefficients will be generated after this step: the approximation coefficients CA 1 and detail coefficients CD 1 (Attallah et al, 2019).…”

Section: Feature Fusion and Reduction Phasementioning

confidence: 99%

CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes

Attallah

2021

Front. Neuroinform.

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Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based on either deep learning (DL) or textural analysis feature extractions. Also, such studies employed distinct features to accomplish the classification procedure. Besides, most of them only extracted spatial features. Nevertheless, CoMB-Deep blends the advantages of textural analysis feature extraction techniques and DL approaches. The CoMB-Deep consists of a composite of DL techniques. Initially, it extracts deep spatial features from 10 convolutional neural networks (CNNs). It then performs a feature fusion step using discrete wavelet transform (DWT), a texture analysis method capable of reducing the dimension of fused features. Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. A bi-directional long-short term memory (Bi-LSTM) network; a DL-based approach that is utilized for the classification phase. CoMB-Deep maintains two classification categories: binary category for distinguishing between the abnormal and normal cases and multi-class category to identify the subclasses of MB. The results of the CoMB-Deep for both classification categories prove that it is reliable. The results also indicate that the feature sets selected using both search strategies have enhanced the performance of Bi-LSTM compared to individual spatial deep features. CoMB-Deep is compared to related studies to verify its competitiveness, and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can help pathologists perform accurate diagnoses, reduce misdiagnosis risks that could occur with manual diagnosis, accelerate the classification procedure, and decrease diagnosis costs.

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“…In whole-trial decoding, components of event-related potentials (ERPs) such as N1, P1, P2a, and P2b, which quantify time-specific variabilities of within-trial activation, have provided significant information about object categories (separately and in combination; Chan, Halgren, Marinkovic, & Cash, 2011;Wang, Xiong, Hu, Yao, & Zhang, 2012;Qin et al, 2016). Others successfully decoded information from more complex variance-and frequencybased features such as signal phase (Behroozi, Daliri, & Shekarchi, 2016;Watrous, Deuker, Fell, & Axmacher, 2015;Torabi, Jahromy, & Daliri, 2017;Wang, Wang, & Yu, 2018;Voloh, Oemisch, & Womelsdorf, 2020), signal power across frequency bands (Rupp et al, 2017;Miyakawa et al, 2018;Majima et al, 2014), time-frequency wavelet coefficients (Hatamimajoumerd & Talebpour, 2019;Taghizadeh-Sarabi, Daliri, & Niksirat, 2015), interelectrode temporal correlations (Karimi-Rouzbahani, Bagheri, & Ebrahimpour, 2017a), and information-based features (e.g., entropy; Joshi, Panigrahi, Anand, & Santhosh, 2018;Torabi et al, 2017;Stam, 2005). Therefore, the neural codes are generally detected from EEG activity using a wide range of features sensitive to temporal variability.…”

Section: Introductionmentioning

confidence: 99%

Temporal Variabilities Provide Additional Category-Related Information in Object Category Decoding: A Systematic Comparison of Informative EEG Features

et al. 2021

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How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly uses the mean neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability that is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional mean activity. Here we compare the information content of 31 variability-sensitive features against the mean of activity, using three independent highly varied data sets. In whole-trial decoding, the classical event-related potential (ERP) components of P2a and P2b provided information comparable to those provided by original magnitude data (OMD) and wavelet coefficients (WC), the two most informative variability-sensitive features. In time-resolved decoding, the OMD and WC outperformed all the other features (including the mean), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in the theta frequency band, previously suggested to support feedforward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously such as phase, amplitude, and frequency rather than mean per se. In our active categorization data set, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.

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A Temporal Neural Trace of Wavelet Coefficients in Human Object Vision: An MEG Study

Cited by 10 publications

References 38 publications

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes

Temporal Variabilities Provide Additional Category-Related Information in Object Category Decoding: A Systematic Comparison of Informative EEG Features

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