Histological image color normalization using a skewed normal distribution mixed model.

Color variation between histological images may influence the performance of computer-aided histological image analysis. Therefore, among the most essential and challenging tasks in histological image analysis are the reduction of the color variation between images and the preservation of the histological information contained in the images. In recent years, many methods have been introduced with respect to the color normalization of histological images. In this study, we introduce a new clustering method referred to as the skewed normal distribution mixed model clustering algorithm. Realizing that the color distribution of hue values approximates the combination of several skewed normal distributions, we propose to use the skewed normal distribution mixture model to analyze the hue distribution. The proposed skewed normal distribution mixture model clustering algorithm includes saturation-weighted hue histograms because it takes into account the saturation and hue information of a particular histogram image, which can diminish the influence of achromatic pixels. Finally, we conducted extensive experiments based on three data sets and compared them with commonly used color normalization methods. The experiments show that the proposed algorithm has better performance in stain separation and color normalization compared to other methods.

PCformer: an MVI recognition method via classification of the MVI boundary according to histopathological images of liver cancer.

Liver cancer is one of the most common cancers leading to death in the world. Microvascular invasion (MVI) is a principal reason for the poor long-term survival rate after liver cancer surgery. Early detection and treatment are very important for improving the survival rate. Manual examination of MVI based on histopathological images is very inefficient and time consuming. MVI automatic diagnosis based on deep learning methods can effectively deal with this problem, reduce examination time, and improve detection efficiency. In recent years, deep learning-based methods have been widely used in histopathological image analysis because of their impressive performance. However, it is very challenging to identify MVI directly using deep learning methods, especially under the interference of hepatocellular carcinoma (HCC) because there is no obvious difference in the histopathological level between HCC and MVI. To cope with this problem, we adopt a method of classifying the MVI boundary to avoid interference from HCC. Nonetheless, due to the specificity of the histopathological tissue structure with the MVI boundary, the effect of transfer learning using the existing models is not obvious. Therefore, in this paper, according to the features of the MVI boundary histopathological tissue structure, we propose a new classification model, i.e., the PCformer, which combines the convolutional neural network (CNN) method with a visual transformer and improves the recognition performance of the MVI boundary histopathological image. Experimental results show that our method has better performance than other models based on a CNN or a transformer.

Instantaneous estimation of momentary affective responses using neurophysiological signals and a spatiotemporal emotional intensity regression network.

Previous studies in affective computing often use a fixed emotional label to train an emotion classifier with electroencephalography (EEG) from individuals experiencing an affective stimulus. However, EEGs encode emotional dynamics that include varying intensities within a given emotional category. To investigate these variations in emotional intensity, we propose a framework that obtains momentary affective labels for fine-grained segments of EEGs with human feedback. We then model these labeled segments using a novel spatiotemporal emotional intensity regression network (STEIR-Net). It integrates temporal EEG patterns from nine predefined cortical regions to provide a continuous estimation of emotional intensity. We demonstrate that the STEIR-Net outperforms classical regression models by reducing the root mean square error (RMSE) by an average of 4∼9 % and 2∼4 % for the SEED and SEED-IV databases, respectively. We find that the frontal and temporal cortical regions contribute significantly to the affective intensity's variation. Higher absolute values of the Spearman correlation coefficient between the model estimation and momentary affective labels under happiness (0.2114) and fear (0.2072) compared to neutral (0.1694) and sad (0.1895) emotions were observed. Besides, increasing the input length of the EEG segments from 4 to 20 s further reduces the RMSE from 1.3548 to 1.3188.

A fully automatic deep learning-based method for segmenting regions of interest and predicting renal function in pediatric dynamic renal scintigraphy.

OBJECTIVE: Accurate delineation of renal regions of interest (ROIs) is critical for the assessment of renal function in pediatric dynamic renal scintigraphy (DRS). The purpose of this study was to develop and evaluate a deep learning (DL) model that can fully automatically delineate renal ROIs and calculate renal function in pediatric 99mTechnetium-ethylenedicysteine (99mTc-EC) DRS. METHODS: This study retrospectively analyzed 1,283 pediatric DRS data at a single center from January to December 2018. These patients were divided into training set (n = 1027), validation set (n = 128), and testing set (n = 128). A fully automatic segmentation of ROIs (FASR) model was developed and evaluated. The pixel values of the automatically segmented ROIs were calculated to predict renal blood perfusion rate (BPR) and differential renal function (DRF). Precision, recall rate, intersection over union (IOU), and Dice similarity coefficient (DSC) were used to evaluate the performance of FASR model. Intraclass correlation (ICC) and Pearson correlation analysis were used to compare the consistency of automatic and manual method in assessing the renal function parameters in the testing set. RESULTS: The FASR model achieved a precision of 0.88, recall rate of 0.94, IOU of 0.83, and DSC of 0.91. In the testing set, the r values of BPR and DRF calculated by the two methods were 0.94 (P < 0.01) and 0.97 (P < 0.01), and the ICCs (95% confidence interval CI) were 0.94 (0.90-0.96) and 0.94 (0.91-0.96). CONCLUSION: We propose a reliable and stable DL model that can fully automatically segment ROIs and accurately predict renal function in pediatric 99mTc-EC DRS.

Identification of human mental workload levels in a language comprehension task with imbalance neurophysiological data.

BACKGROUND AND OBJECTIVE: Operator's capability for accurately comprehending verbal commands is critically important to maintain the performance of human-machine interaction. It can be evaluated by human mental workload measured with electroencephalography (EEG). However, the time duration of different workload conditions within a task session is unequal due to varied psychophysiological processes across individuals. It leads to data imbalance of the EEG for training workload classifiers. METHODS: In this study, we propose an EEG feature oversampling technique, Gaussian-SMOTE based feature ensemble (GSMOTE-FE), for workload recognition with imbalanced classes. First, artificial EEG instances are drawn from a Gaussian distribution in the margin between the minority and majority workload classes. Tomek links are detected as clues to remove redundant feature vectors. Then, we embed a feature selection module based on the GINI importance while an ensemble classifier committee with bootstrap aggregating is used to further enhance classification performance. RESULTS: We validate the GSMOTE-FE framework based on an experiment that simulates operators to understand the correct meaning of the instructions in the Chinese language. Participants' EEG signals and reaction time data were both recorded to validate the proposed workload classifier. Workload classification accuracy and Macro-F1 values are 0.6553 and 0.5862, respectively. Corresponding G-mean and AUC achieve at 0.5757 and 0.5958, respectively. CONCLUSIONS: The performance of the GSMOTE-FE is demonstrated to be comparable with the advanced oversampling techniques. The workload classifier has the capability to indicate low and high levels of the task demand of the Chinese language understanding task.

Selecting transferrable neurophysiological features for inter-individual emotion recognition via a shared-subspace feature elimination approach.

The interplay between human emotions, personality, and motivation results in individual specificity in neurophysiological data distributions for the same emotional category. To address this issue for building an emotion recognition system based on electroencephalogram (EEG) features, we propose a shared-subspace feature elimination (SSFE) approach to identify EEG variables with common characteristics across multiple individuals. In the SSFE framework, a low-dimensional space defined by a selected number of EEG features is created to represent the inter-emotion discriminant for different pairs of subjects evaluated based on the interclass margin. Using two public databases-DEAP and MAHNOB-HCI-the performance of the SSFE is validated according to the leave-one-subject-out paradigm. The performance of the proposed framework is compared with five other feature-selection methods. The effectiveness and computational cost of the SSFE is investigated across six machine learning models based on their optimal hyperparameters. In the end, the competitive binary classification accuracy from the SSFE of arousal and valence recognitions are determined to be 0.6521 and 0.6635, respectively, for DEAP, and 0.6520 and 0.6537, respectively for MAHNOB-HCI.

[Feature selection based on correlation degree and its application in traditional Chinese medicine].

Mutual information can measure arbitrary statistical dependencies. It has been applied to many kinds of fields widely. But when mutual information is used as the correlation measure, the features with more values are apt to be chosen. To solve this problem, a novel definition of correlation degree is proposed in this paper. It can avoid the shortcoming of selecting more value features when mutual information acted as the measure, and it can avoid the shortcoming of selecting less value features when correlation degree coefficients acted as the measure. In the method using the novel definition, the number of selected features is determined by the correct classification rate of Support Vector Machine. At last, the efficiency of the method is illustrated through analyzing the symptoms combination of seven essential elements of the syndrome corresponding to stroke.