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Citations
... To ensure that the samples in each category contribute relatively equally to the loss function, a weighted cross-entropy loss function [31] is established, assigning weight coefficients to each category according to the size of the sample pool: ...
Lightweight LiDAR, characterized by its ease of use and cost-effectiveness, offers advantages in road intersection information acquisition. This study used lightweight LiDAR to collect 3D point cloud data from an urban road intersection and propose a semantic segmentation model based on the improved RandLA-Net. Initially, raw data from multiple positions and perspectives were obtained, and complete road intersection point clouds were stitched together using the iterative closest point algorithm for sequential registration. Subsequently, a semantic segmentation method for point clouds based on the improved RandLA-Net was proposed. This method included a spatial information encoding module based on feature similarities and a feature enhancement module based on multi-pooling fusion. This model optimized the feature aggregation capabilities during downsampling with the weighted cross-entropy loss function applied to reduce the impact of input sample scale imbalances. In comparisons of the improved RandLA-Net with PointNet++ and RandLA-Net on the same dataset, our method showed improved segmentation accuracy for various categories. The overall prediction accuracy on two road intersection point cloud test sets was 87.68% and 89.61%, with average F1 scores of 82.76% and 80.61%, respectively. Most notably, the prediction accuracy for road surface areas reached 94.48% and 94.79%. The results show that our model can enrich the spatial feature expression of input data and enhance semantic segmentation performance in road intersection scenarios.
... A number of attempts have been made to introduce weights into the relative entropy [23][24][25][26][27][28][29]. Some of these go back to [30,31]. ...
The concept of attention, numerical weights that emphasize the importance of particular data, has proven to be very relevant in artificial intelligence. Relative entropy (RE, aka Kullback-Leibler divergence) plays a central role in communication theory. Here we combine these concepts, attention and RE. RE guides optimal encoding of messages in bandwidth-limited communication as well as optimal message decoding via the maximum entropy principle (MEP). In the coding scenario, RE can be derived from four requirements, namely being analytical, local, proper, and calibrated. Weighted RE, used for attention steering in communications, turns out to be improper. To see how proper attention communication can emerge, we analyze a scenario of a message sender who wants to ensure that the receiver of the message can perform well-informed actions. If the receiver decodes the message using the MEP, the sender only needs to know the receiver's utility function to inform optimally, but not the receiver's initial knowledge state. In case only the curvature of the utility function maxima are known, it becomes desirable to accurately communicate an attention function, in this case a by this curvature weighted and re-normalized probability function. Entropic attention communication is here proposed as the desired generalization of entropic communication that permits weighting while being proper, thereby aiding the design of optimal communication protocols in technical applications and helping to understand human communication. For example, our analysis shows how to derive the level of cooperation expected under misaligned interests of otherwise honest communication partners.
... given by Zhou et al. (2021), where ( ) and ( ) denote real and predicted distributions, and is the number of classes. Layers from the first convolutional to the second last Dense layer, generally called hidden layers, consist of 5 trainable and 6 nontrainable layers. ...
We present an image classification algorithm using deep learning convolutional neural network architecture, which classifies the morphologies of eclipsing binary systems based on their light curves. The algorithm trains the machine with light curve images generated from the observational data of eclipsing binary stars in contact, detached and semi-detached morphologies, whose light curves are provided by Kepler, ASAS and CALEB catalogues. The structure of the architecture is explained, the parameters of the network layers and the resulting metrics are discussed. Our results show that the algorithm, which is selected among 132 neural network architectures, estimates the morphological classes of an independent validation dataset, 705 real data, with an accuracy of 92%.
... However, 5002 positive association subsets and 86,348 negative association subsets, can affect the calculation of most losses, which are difficult to provide useful information. To solve the problem of unbalanced positive and negative samples, we choose the weighted cross-entropy loss function [31], which is shown as follows: ...
Background
Piwi-interacting RNAs (piRNAs) have been proven to be closely associated with human diseases. The identification of the potential associations between piRNA and disease is of great significance for complex diseases. Traditional “wet experiment” is time-consuming and high-priced, predicting the piRNA-disease associations by computational methods is of great significance.
Methods
In this paper, a method based on the embedding transformation graph convolution network is proposed to predict the piRNA-disease associations, named ETGPDA. Specifically, a heterogeneous network is constructed based on the similarity information of piRNA and disease, as well as the known piRNA-disease associations, which is applied to extract low-dimensional embeddings of piRNA and disease based on graph convolutional network with an attention mechanism. Furthermore, the embedding transformation module is developed for the problem of embedding space inconsistency, which is lightweighter, stronger learning ability and higher accuracy. Finally, the piRNA-disease association score is calculated by the similarity of the piRNA and disease embedding.
Results
Evaluated by fivefold cross-validation, the AUC of ETGPDA achieves 0.9603, which is better than the other five selected computational models. The case studies based on Head and neck squamous cell carcinoma and Alzheimer’s disease further prove the superior performance of ETGPDA.
Conclusions
Hence, the ETGPDA is an effective method for predicting the hidden piRNA-disease associations.
... However, in crack detection [30], it has been found that adding larger weights to the cracks results in more false positives. In order to tackle both types of imbalance during training and inference, we introduce a hybrid loss function consisting of contributions from both dice loss [77] and cross-entropy loss [78]. Specifically, the dice loss (Equation (1)) learns the class distribution, alleviating the imbalance problem, while the cross-entropy loss (Equation (2)) is used to penalize false positives/negatives while performing curve smoothing at the same time. ...
The lack of large-scale, multi-scene, and multi-type pavement distress training data reduces the generalization ability of deep learning models in complex scenes, and limits the development of pavement distress extraction algorithms. Thus, we built the first large-scale dichotomous image segmentation (DIS) dataset for multi-type pavement distress segmentation, called ISTD-PDS7, aimed to segment highly accurate pavement distress types from natural charge-coupled device (CCD) images. The new dataset covers seven types of pavement distress in nine types of scenarios, along with negative samples with texture similarity noise. The final dataset contains 18,527 images, which is many more than the previously released benchmarks. All the images are annotated with fine-grained labels. In addition, we conducted a large benchmark test, evaluating seven state-of-the-art segmentation models, providing a detailed discussion of the factors that influence segmentation performance, and making cross-dataset evaluations for the best-performing model. Finally, we investigated the effectiveness of negative samples in reducing false positive prediction in complex scenes and developed two potential data augmentation methods for improving the segmentation accuracy. We hope that these efforts will create promising developments for both academics and the industry.
... For data segmentation, delineating the region of interest (ROI) is one of the essential steps in image processing to focus the center of attention on the clinically relevant regions and to avoid irrelevant image area information from degrading the efficiency and accuracy of model training. The procedures were often conducted by manually contouring the tumor boundary by radiologists with the assistance of software [42,53,54,59]. Alternatively, Pereira et al. [49] applied a threshold-based method to segment the regions with elastographic stress higher than 70% of the maximum stress, but this threshold level was not justified. ...
Simple Summary
The incidence of endocrine cancers (e.g., thyroid, pancreas, and adrenal) has been increasing; these cancers have a high premature mortality rate. Traditional medical imaging methods (e.g., MRI and CT) might not be sufficient for accurate cancer screening. Elastography complements conventional medical imaging modalities by identifying abnormal tissue stiffness of the tumor, in which machine learning techniques can further improve accuracy and reliability. This review focuses on the applications and performance of machine-learning-based elastography in classifying endocrine tumors.
Abstract
Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN–long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images.
... In the minibatch (size = 16) for learning, the ratio of the positive and negative images was set to 1:1. The weighted Cross-Entropy loss function was used to solve the negative effect of overfitting on the training dataset on the accuracy of the deep learning model due to a decrease in the imbalance of the convergence speed of the loss function 23 . The following standard weighted binary cross-entropy loss function was used: www.nature.com/scientificreports/ ...
Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase the accuracy of diagnosis of acute thoracic aortic dissection based on chest x-rays by applying deep learning techniques. In aggregate, 3,331 images, comprising 716 positive images and 2615 negative images, were collected from 3,331 patients. Residual neural network 18 was used to detect acute thoracic aortic dissection. The diagnostic accuracy of the ResNet18 was observed to be 90.20% with a precision of 75.00%, recall of 94.44%, and F1-score of 83.61%. Further research is required to improve diagnostic accuracy based on aorta segmentation.
... Computational Intelligence and Neuroscience displayed in Figure 3. From P-Net to R-Net to O-Net, as the size of the input image is getting larger and the network structure is getting deeper and deeper, the obtained feature information is more and more expressive [21]. When training the MTCNN network, the following three main tasks must be converged: face probability, the position of the face candidate frame, and the five marker determination points of the face [22]. e following cross-entropy loss function is used for the face, as expressed in ...
The objectives are to solve the problems existing in the current ideological and political theory courses, such as the difficulty of classroom teaching quality assessment, the confusion of teachers’ classroom process management, and the lack of objective assessment basis in teaching quality monitoring. Based on Artificial Intelligence (AI) technology, a designed evaluation method is proposed for teachers’ classroom teaching and solves some problems such as high system cost, low evaluation accuracy, and imperfect evaluation methods. Firstly, the boundary algorithm system is introduced in the research, and the Field Programmable Gate Array (FPGA) by deep learning (DL) is used to accelerate the server hardware network platform and equipped with pan tilt zoom (PTZ) and manage multiple AI + embedded visual boundary algorithm devices. Secondly, the network platform can manage the PTZ and focal length of Internet protocol (IP) cameras, measure, and capture face images, transmit data, and recognize students’ face, head, and body postures. Finally, classroom teaching is evaluated, and students’ behavioral data and functions are designed, debugged, and tested. The research results demonstrate that the method overcomes the problem of high system cost through edge computing and hardware structure, and DL technology is used to overcome the problem of low accuracy of classroom teaching evaluation. Various indicators such as attendance rate, concentration, activity, and richness of teaching links in classroom teaching are obtained. The method involved can make an objective evaluation of classroom teaching and overcome the problem of incomplete classroom teaching evaluation.
... In the mini-batch (size=16) for learning, the ratio of the positive and negative images was set to 1:1. The weighted Cross-Entropy loss function was used to solve the problem that the accuracy of the deep learning model over tting on the training dataset due to the imbalance of the convergence speed of the loss function decreases 15 . The standard weighted binary cross-entropy loss function is given by: ...
... In the mini-batch (size = 16) for learning, the ratio of the positive and negative images was set to 1:1. The weighted Cross-Entropy loss function was used to solve the problem that the accuracy of the deep learning model over tting on the training dataset due to the imbalance of the convergence speed of the loss function decreases 15 . The standard weighted binary cross-entropy loss function is given by: ...
Aortic dissection is one of the most life-threatening acute aortic syndromes in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. With the recent development of deep learning technology for image recognition, we developed a deep learning algorithm for screening aortic dissection through chest X-ray scans using a convoluted neural network and evaluated the diagnostic ability of the developed algorithm. The chest X-ray images were obtained from three tertiary academic hospitals. After learning using residual neural network 18- and 5-fold cross-validation with chest X-ray images obtained from two hospitals, a test was performed with data from the remaining one hospital. To validate the performance of five models trained through 5-fold cross-validation, accuracy, precision, recall, and F-1 score were calculated. A total of 3,331 images containing 716 positive images and 2615 negative images were collected from 3,331 patients. Overall, 1,972 images consisting of 507 positive images (male, 62.7%; age [SD], 61 [15] years) in hospital A, 1,155 images consisting of 155 positive images (male, 56.1%; age [SD], 63 [13] years), and 204 images consisting of 54 positive images (male, 55.6%; age [SD], 61 [17] years) were analyzed. The diagnostic accuracy of the deep learning model was 90.20% with precision 75.00%, recall 94.44%, and F1-score 83.61%. In conclusion, the interpretation of chest X-ray images using the CNN algorithm that we developed to detect aortic dissection could help doctors screen patients with suspected aortic dissection.
... In this case, the prediction accuracy of the class with greater data is higher than that of the class with less data. Therefore, the ideal case would be for each class to have approximately the same amount of data [28,29]. ...
Light detection and ranging (LiDAR) data of 3D point clouds acquired from laser sensors is a crucial form of geospatial data for recognition of complex objects since LiDAR data provides geometric information in terms of 3D coordinates with additional attributes such as intensity and multiple returns. In this paper, we focused on utilizing multiple returns in the training data for semantic segmentation, in particular building extraction using PointNet++. PointNet++ is known as one of the efficient and robust deep learning (DL) models for processing 3D point clouds. On most building boundaries, two returns of the laser pulse occur. The experimental results demonstrated that the proposed approach could improve building extraction by adding two returns to the training datasets. Specifically, the recall value of the predicted building boundaries for the test data was improved from 0.7417 to 0.7948 for the best case. However, no significant improvement was achieved for the new data because the new data had relatively lower point density compared to the training and test data.
