Electrical Properties of Human Lung Nodules In Vitro From 100 Hz to 100 MHz.

OBJECTIVE: The incidence of pulmonary nodules has been increasing over the past 30 years. Different types of nodules are associated with varying degrees of malignancy, and they engender inconsistent treatment approaches. Therefore, correct distinction is essential for the optimal treatment and recovery of the patients. The commonly-used medical imaging methods have limitations in distinguishing lung nodules to date. A new approach to this problem may be provided by electrical properties of lung nodules. Nevertheless, difference identification is the basis of correct distinction. So, this paper aims to investigate the differences in electrical properties between various lung nodules. METHODS: At variance with existing studies, benign samples were included for analysis. A total of 252 specimens were collected, including 126 normal tissues, 15 benign nodules, 76 adenocarcinomas, and 35 squamous cell carcinomas. The dispersion properties of each tissue were measured over a frequency range of 100 Hz to 100 MHz. And the relaxation mechanism was analyzed by fitting the Cole-Cole plot. The corresponding equivalent circuit was estimated accordingly. RESULTS: Results validated the significant differences between malignant and normal tissue. Significant differences between benign and malignant lesions were observed in conductivity and relative permittivity. Adenocarcinomas and squamous cell carcinomas are significantly different in conductivity, first-order, second-order differences of conductivity, α-band Cole-Cole plot parameters and capacitance of equivalent circuit. The combination of the different features increased the tissue groups' differences measured by Euclidean distance up to 94.7%. CONCLUSION AND SIGNIFICANCE: In conclusion, the four tissue groups reveal dissimilarity in electrical properties. This characteristic potentially lends itself to future diagnosis of non-invasive lung cancer.

Training-Free Deep Generative Networks for Compressed Sensing of Neural Action Potentials.

Energy consumption is an important issue for resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) is a promising framework for addressing this challenge because it can compress data in an energy-efficient way. Recent work has shown that deep neural networks (DNNs) can serve as valuable models for CS of neural action potentials (APs). However, these models typically require impractically large datasets and computational resources for training, and they do not easily generalize to novel circumstances. Here, we propose a new CS framework, termed APGen, for the reconstruction of APs in a training-free manner. It consists of a deep generative network and an analysis sparse regularizer. We validate our method on two in vivo datasets. Even without any training, APGen outperformed model-based and data-driven methods in terms of reconstruction accuracy, computational efficiency, and robustness to AP overlap and misalignment. The computational efficiency of APGen and its ability to perform without training make it an ideal candidate for long-term, resource-constrained, and large-scale wireless neural recording. It may also promote the development of real-time, naturalistic brain-computer interfaces.

A gradient-based automatic optimization CNN framework for EEG state recognition.

Objective. The electroencephalogram (EEG) signal, as a data carrier that can contain a large amount of information about the human brain in different states, is one of the most widely used metrics for assessing human psychophysiological states. Among a variety of analysis methods, deep learning, especially convolutional neural network (CNN), has achieved remarkable results in recent years as a method to effectively extract features from EEG signals. Although deep learning has the advantages of automatic feature extraction and effective classification, it also faces difficulties in network structure design and requires an army of prior knowledge. Automating the design of these hyperparameters can therefore save experts' time and manpower. Neural architecture search techniques have thus emerged.Approach. In this paper, based on an existing gradient-based neural architecture search (NAS) algorithm, partially-connected differentiable architecture search (PC-DARTS), with targeted improvements and optimizations for the characteristics of EEG signals. Specifically, we establish the model architecture step by step based on the manually designed deep learning models for EEG discrimination by retaining the framework of the search algorithm and performing targeted optimization of the model search space. Corresponding features are extracted separately according to the frequency domain, time domain characteristics of the EEG signal and the spatial position of the EEG electrode. The architecture was applied to EEG-based emotion recognition and driver drowsiness assessment tasks.Main results. The results illustrate that compared with the existing methods, the model architecture obtained in this paper can achieve competitive overall accuracy and better standard deviation in both tasks.Significance. Therefore, this approach is an effective migration of NAS technology into the field of EEG analysis and has great potential to provide high-performance results for other types of classification and prediction tasks. This can effectively reduce the time cost for researchers and facilitate the application of CNN in more areas.

Multi-Stream Fusion Network With Generalized Smooth L1 Loss for Single Image Dehazing.

Single image dehazing is an important but challenging computer vision problem. For the problem, an end-to-end convolutional neural network, named multi-stream fusion network (MSFNet), is proposed in this paper. MSFNet is built following the encoder-decoder network structure. The encoder is a three-stream network to produce features at three resolution levels. Residual dense blocks (RDBs) are used for feature extraction. The resizing blocks serve as bridges to connect different streams. The features from different streams are fused in a full connection manner by a feature fusion block, with stream-wise and channel-wise attention mechanisms. The decoder directly regresses the dehazed image from coarse to fine by the use of RDBs and the skip connections. To train the network, we design a generalized smooth L1 loss function, which is a parametric loss family and permits to adjust the insensitivity to the outliers by varying the parameter settings. Moreover, to guide MSFNet to capture the valid features in each stream, we propose the multi-scale supervision learning strategy, where the loss at each resolution level is computed and summed as the final loss. Extensive experimental results demonstrate that the proposed MSFNet achieves superior performance on both synthetic and real-world images, as compared with the state-of-the-art single image dehazing methods.

An efficient ensemble method for missing value imputation in microarray gene expression data.

BACKGROUND: The genomics data analysis has been widely used to study disease genes and drug targets. However, the existence of missing values in genomics datasets poses a significant problem, which severely hinders the use of genomics data. Current imputation methods based on a single learner often explores less known genomic data information for imputation and thus causes the imputation performance loss. RESULTS: In this study, multiple single imputation methods are combined into an imputation method by ensemble learning. In the ensemble method, the bootstrap sampling is applied for predictions of missing values by each component method, and these predictions are weighted and summed to produce the final prediction. The optimal weights are learned from known gene data in the sense of minimizing a cost function about the imputation error. And the expression of the optimal weights is derived in closed form. Additionally, the performance of the ensemble method is analytically investigated, in terms of the sum of squared regression errors. The proposed method is simulated on several typical genomic datasets and compared with the state-of-the-art imputation methods at different noise levels, sample sizes and data missing rates. Experimental results show that the proposed method achieves the improved imputation performance in terms of the imputation accuracy, robustness and generalization. CONCLUSION: The ensemble method possesses the superior imputation performance since it can make use of known data information more efficiently for missing data imputation by integrating diverse imputation methods and learning the integration weights in a data-driven way.

Training-free compressed sensing for wireless neural recording using analysis model and group weighted [Formula: see text]-minimization.

OBJECTIVE: Data compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. In this paper, an analytical, training-free CS recovery method, termed group weighted analysis [Formula: see text]-minimization (GWALM), is proposed for wireless neural recording. APPROACH: The GWALM method consists of three parts: (1) the analysis model is adopted to enforce sparsity of the neural signals, therefore overcoming the drawbacks of conventional synthesis models and enhancing the recovery performance. (2) A multi-fractional-order difference matrix is constructed as the analysis operator, thus avoiding the dictionary learning procedure and reducing the need for previously acquired data and computational complexities. (3) By exploiting the statistical properties of the analysis coefficients, a group weighting approach is developed to enhance the performance of analysis [Formula: see text]-minimization. MAIN RESULTS: Experimental results on synthetic and real datasets reveal that the proposed approach outperforms state-of-the-art CS-based methods in terms of both spike recovery quality and classification accuracy. SIGNIFICANCE: Energy and area efficiency of the GWALM make it an ideal candidate for resource-constrained, large scale wireless neural recording applications. The training-free feature of the GWALM further improves its robustness to spike shape variation, thus making it more practical for long term wireless neural recording.