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Background: Breast cancer (BC) is among the most common cancers worldwide. Machine learning–based radiomics model could predict axillary lymph node metastasis (ALNM) of BC accurately. Purpose: The purpose is to develop a machine learning model to predict ALNM of BC by focusing on the radiomics features of axillary lymphatic node (ALN). Methods: A group of 398 BC patients with 800 ALNs were retrospectively collected. A set of patient characteristics were obtained to form clinical factors. Three hundred and twenty‐six radiomics features were extracted from each region of interest for ALN in contrast‐enhanced computed tomography (CECT) image. A framework composed of four feature selection methods and 14 machine learning classification algorithms was systematically applied. A clinical model, a radiomics model, and a combined model were developed using a cross‐validation approach and compared. Metrics of the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the performance of these models in the prediction of ALNM in BC. Results: Among the 800 cases of ALNs, there were 388 cases of positive metastasis (48.50%) and 412 cases of negative metastasis (51.50%). The baseline clinical model achieved the performance with an AUC = 0.8998 (95% CI [0.8540, 0.9457]). The radiomics model achieved an AUC = 0.9081 (95% CI [0.8640, 0.9523]). The combined model using the clinical factors and radiomics features achieved the best results with an AUC = 0.9305 (95% CI [0.8928, 0.9682]). Conclusions: Combinations of feature selection methods and machine learning‐based classification algorithms can develop promising predictive models to predict ALNM in BC using CECT features. The combined model of clinical factors and radiomics features outperforms both the clinical model and the radiomic model. [ABSTRACT FROM AUTHOR]

Background: The use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately. Purpose: The aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer. Methods: We retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC). Results: The radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p =0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p =0.005) for predicting ALNM in breast cancer. Conclusion: The radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis. [ABSTRACT FROM AUTHOR]

Background: Drug development is an expensive and time-consuming process. Literature-based discovery has played a critical role in drug development and may be a supplementary method to help scientists speed up the discovery of drugs.Methods: Here, we propose a relation path features embedding based convolutional neural network model with attention mechanism for drug discovery from literature, which we denote as PACNN. First, we use predications from biomedical abstracts to construct a biomedical knowledge graph, and then apply a path ranking algorithm to extract drug-disease relation path features on the biomedical knowledge graph. After that, we use these drug-disease relation features to train a convolutional neural network model which combined with the attention mechanism. Finally, we employ the trained models to mine drugs for treating diseases.Results: The experiment shows that the proposed model achieved promising results, comparing to several random walk algorithms.Conclusions: In this paper, we propose a relation path features embedding based convolutional neural network with attention mechanism for discovering potential drugs from literature. Our method could be an auxiliary method for drug discovery, which can speed up the discovery of new drugs for the incurable diseases. [ABSTRACT FROM AUTHOR]

• Four types of defects in rubber timber are performed by improved YOLOX. • Improve the feature fusion module of YOLOX by adding ECA attention mechanism and ASSF multi-feature adaptive fusion. • Improve the confidence loss function and change BCE loss to Focal loss. • The regression of the target box was performed using EIOU loss. Deep learning has achieved certain results in the field of wood surface defect detection. To address the problems of low accuracy of the detection results of surface defects on boards, slow detection speed and large number of model parameters, this article take advantage of computer vision to improve the feature fusion module of YOLOX target detection algorithm, by adding efficient channel attention (ECA) mechanism, adaptive spatial feature fusion mechanism (ASFF) and improve the confidence loss and localization loss functions as Focal loss and Efficient Intersection over Union (EIoU) loss, to enhance the feature extraction ability and detection accuracy of the algorithm. Considering the depth and width of the model, the depth-separable convolution and optional multi-version algorithm are used to reduce the model parameters and computational effort to seek the optimal model. Experiments show that the improved model detects four types of defects in rubber timber with a considerable improvement and has significant advantages over other target detection algorithms. [ABSTRACT FROM AUTHOR]

• Based on image feature extraction technology to build aggregate feature data set. • A fusion feature importance analysis method is proposed. • Establish a random forest and XGBoost aggregate shape classification model. • The ability to recognize the 3D features of aggregates in 2D images are improved. Aggregate plays the role of skeleton filling in asphalt pavements. The shape of the aggregate affects the embedded structure between the aggregates, thus affecting the performance of asphalt concrete. In this study, extreme gradient boosting (XGBoost) classification is used to study the automatic shape classification of aggregates. The expression of main and microscopic features of aggregate was improved by transforming aggregate images into data, and a feature importance analysis method based on method fusion is proposed to select the feature parameters of aggregate morphology. Based on cross-validation, the XGBoost classification model was trained by optimizing the super parameter combination to complete the classification of aggregate shapes. Compared with the random forest model, the results show that the proposed method can effectively classify aggregate shapes. It is also proved that the two-dimensional images can reflect the three-dimensional features of the aggregate to some extent. This method provides a certain theoretical basis for the automatic classification of aggregate, and simultaneously it has important practical significance to promote the intelligent production of asphalt mixtures. [ABSTRACT FROM AUTHOR]