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Context: Software defect prediction plays a crucial role in estimating the most defect-prone components of software, and a large number of studies have pursued improving prediction accuracy within a project or across projects. However, the rules for making an appropriate decision between within-and cross-project defect prediction when available historical data are insufficient remain unclear.Objective: The objective of this work is to validate the feasibility of the predictor built with a simplified metric set for software defect prediction in different scenarios, and to investigate practical guidelines for the choice of training data, classifier and metric subset of a given project. Method: First, based on six typical classifiers, three types of predictors using the size of software metric set were constructed in three scenarios. Then, we validated the acceptable performance of the predictor based on Top-k metrics in terms of statistical methods. Finally, we attempted to minimize the Top-k metric subset by removing redundant metrics, and we tested the stability of such a minimum metric subset with one-way ANOVA tests. Results: The study has been conducted on 34 releases of 10 open-source projects available at the PROMISE repository. The findings indicate that the predictors built with either Top-k metrics or the minimum metric subset can provide an acceptable result compared with benchmark predictors. The guideline for choosing a suitable simplified metric set in different scenarios is presented in Table 12. Conclusion: The experimental results indicate that (1) the choice of training data for defect prediction should depend on the specific requirement of accuracy; (2) the predictor built with a simplified metric set works well and is very useful in case limited resources are supplied; (3) simple classifiers (e.g., Naïve Bayes) also tend to perform well when using a simplified metric set for defect prediction; (4) in several cases, the minimum metric subset can be identified to facilitate the procedure of general defect prediction with acceptable loss of prediction precision in practice.

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A novel foam formulation with enhanced BMV bioavailability has been shown to be of increased efficacy in the treatment of scalp psoriasis without an associated increase in toxicity.

Background: Uterine cancer, also known as endometrial cancer, can seriously affect the female reproductive organs, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. Computer-aided diagnosis (CADx) approaches based on traditional machine learning algorithms have been proposed to assist pathologists in interpreting histopathological images efficiently. However, due to the limited capability of modeling the complicated relationships between histopathological images and their interpretations, these CADx approaches often failed to achieve satisfying results. Methods: In this study, we developed a CADx approach using a convolutional neural network (CNN) and attention mechanisms, called HIENet. Because HIENet used the attention mechanisms and feature map visualization techniques, it can provide pathologists better interpretability of diagnoses by highlighting the histopathological correlations of local (pixel-level) image features to morphological characteristics of endometrial tissue. We then evaluated the classification performance of HIENet in ten-fold cross-validation on ~3,300 hematoxylin and eosin (H&E) images (collected from ~500 endometrial specimens from October 2017 to August 2018) and external validation on additional 200 H&E images (collected from 50 randomly-selected female patients during the first quarter of 2019). Results: In the ten-fold cross-validation process, the CADx approach achieved a 76.91 ± 1.17% (mean ± s. d.) classification accuracy for four classes of endometrial tissue, namely normal endometrium, endometrial polyp, endometrial hyperplasia, and endometrial adenocarcinoma. Also, HIENet achieved an area-under-the-curve (AUC) of 0.9579 ± 0.0103 with an 81.04 ± 3.87% sensitivity and 94.78 ± 0.87% specificity in a binary classification task that detected endometrioid adenocarcinoma ("Malignant"). Besides, in the external validation process, the CADx approach achieved an 84.50% accuracy in the four-class classification task, and it achieved an AUC of 0.9829 with a 77.97% (95% CI, 65.27%-87.71%) sensitivity and 100% (95% CI, 97.42%-100.00%) specificity. Moreover, positive predictive value (PPV) and negative predictive value (NPV) reached 100% (95% CI, 92.29%-100.00%) and 91.56% (95% CI, 86.00%-95.43%), respectively. The classification performance of HIENet can be further improved if directly trained as a binary classification model (also known as a binary classifier). Conclusion: The proposed CADx approach, HIENet, outperformed three human experts and four end-to-end CNN-based classifiers on this small-scale dataset regarding overall classification performance. It was also able to identify some typical morphological characteristics in H&E images to provide histopathological interpretations for pathologists. KeywordsEndometrial cancer; Hematoxylin and eosin (H&E) images; Deep learning; Class activation map (CAM); Human-machine collaboration. Reshape input target_shape = [-1,256] Reshape input target_shape = [-1,256] MaxPooling1D input strides = [2] pool_siz...