Deep feature representation for the computational analytics of 3D neuronal morphology

Convolutional neural networks (CNN) can learn deep feature representation for hyperspectral imagery (HSI) interpretation and attain excellent accuracy of classification if we have many training samples. Due to its superiority in feature representation, several works focus on it, among which a reliable classification approach based on CNN, used filters generated from cluster framework, like k Means algorithm, yielded good results. However, the kernels number to be manually assigned. To solve this problem, a HSI classification framework based on CNN, where the convolutional filters to be adaptatively learned from the data, by grouping without knowing the cluster number, has recently proposed. This framework, based on the two algorithms CNN and kMeans, showed high accuracy results. So, in the same context, we propose an architecture based on the depth convolution al neural networks principle, where kernels are adaptatively learned, using CkMeans network, to generate filters without knowing the number of clusters, for hyperspectral classification. With adaptive kernels, the proposed framework automatic kernels selection by CkMeans algorithm (AKSCCk) achieves a better classification accuracy compared to the previous frameworks. The experimental results show the effectiveness and feasibility of AKSCCk approach.

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Deep feature representation for hyperspectral image classification

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2015.7326943 ◽

2015 ◽

Cited By ~ 8

Author(s):

Jiming Li ◽

Lorenzo Bruzzone ◽

Sicong Liu

Keyword(s):

Image Classification ◽

Hyperspectral Image ◽

Feature Representation ◽

Hyperspectral Image Classification ◽

Deep Feature

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Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG

Frontiers in Neuroscience ◽

10.3389/fnins.2021.760987 ◽

2021 ◽

Vol 15 ◽

Author(s):

Xincheng Cao ◽

Bin Yao ◽

Binqiang Chen ◽

Weifang Sun ◽

Guowei Tan

Keyword(s):

Short Term Memory ◽

Treatment Plan ◽

Drug Prescription ◽

Feature Representation ◽

Short Term ◽

Accurate Identification ◽

Adversarial Learning ◽

Deep Model ◽

Deep Feature ◽

Seizure Classification

Accurate identification of the type of seizure is very important for the treatment plan and drug prescription of epileptic patients. Artificial intelligence has shown considerable potential in the fields of automated EEG analysis and seizure classification. However, the highly personalized representation of epileptic seizures in EEG has led to many research results that are not satisfactory in clinical applications. In order to improve the clinical adaptability of the algorithm, this paper proposes an adversarial learning-driven domain-invariant deep feature representation method, which enables the hybrid deep networks (HDN) to reliably identify seizure types. In the train phase, we first use the labeled multi-lead EEG short samples to train squeeze-and-excitation networks (SENet) to extract short-term features, and then use the compressed samples to train the long short-term memory networks (LSTM) to extract long-time features and construct a classifier. In the inference phase, we first adjust the feature mapping of LSTM through the adversarial learning between LSTM and clustering subnet so that the EEG of the target patient and the EEG in the database obey the same distribution in the deep feature space. Finally, we use the adjusted classifier to identify the type of seizure. Experiments were carried out based on the TUH EEG Seizure Corpus and CHB-MIT seizure database. The experimental results show that the proposed domain adaptive deep feature representation improves the classification accuracy of the hybrid deep model in the target set by 5%. It is of great significance for the clinical application of EEG automatic analysis equipment.

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