NSSI questionnaires revisited: A data mining approach to shorten the NSSI questionnaires.

BACKGROUND AND OBJECTIVE: Non-suicidal self-injury (NSSI) is a psychological disorder that the sufferer consciously damages their body tissues, often too severe that requires intensive care medicine. As some individuals hide their NSSI behaviors, other people can only identify them if they catch them while injuring, or via dedicated questionnaires. However, questionnaires are long and tedious to answer, thus the answers might be inconsistent. Hence, in this study for the first time, we abstracted a larger questionnaire (of 662 items in total) to own only 22 items (questions) via data mining techniques. Then, we trained several machine learning algorithms to classify individuals based on their answers into two classes. METHODS: Data from 277 previously-questioned participants is used in several data mining methods to select features (questions) that highly represent NSSI, then 245 different people were asked to participate in an online test to validate those features via machine learning methods. RESULTS: The highest accuracy and F1 score of the selected features-via the Genetics algorithm-are 80.0% and 74.8% respectively for a Random Forest algorithm. Cronbach's alpha of the online test (validation on the selected features) is 0.82. Moreover, results suggest that an MLP can classify participants into two classes of NSSI Positive and NSSI Negative with 83.6% accuracy and 83.7% F1-score based on the answers to only 22 questions. CONCLUSION: While previously psychologists used many combined questionnaires to see whether someone is involved in NSSI, via various data mining methods, the present study showed that only 22 questions are enough to predict if someone is involved or not. Then different machine learning algorithms were utilized to classify participants based on their NSSI behaviors, among which, an MLP with 10 hidden layers had the best performance.

IJES-OA Net: A residual neural network to classify knee osteoarthritis from radiographic images based on the edges of the intra-joint spaces.

Among the musculoskeletal disorders in the world, osteoarthritis is the most common, affecting most of the body joints, especially the knee. Clinical radiographic imaging methods are commonly used to diagnose osteoarthritis thanks to their cheapness and availability. Due to the low quality and indiscernibility of these images, however, accurate osteoarthritis diagnosis has always faced inaccuracies, such as the wrong diagnosis. One of the osteoarthritis hallmarks is joint space narrowing. Thus, its degree and severity can be determined relatively by assessing the space between the bones in the joint. Therefore, in this research, a deep residual neural network, termed IJES-OA Net, is presented to automatically grade (classify) the severity of knee osteoarthritis via radiographs. This is achieved by tuning it in a way to have it focused on the distance of the edges of the bones inside the knee joint. Experimental results which are conducted on MOST (for training) and OAI (for validation and testing) datasets show that the IJES-OA Net achieves high average accuracy as well as average precision (80.23% and 0.802, respectively) while having less complexity compared to other methods. Additionally, the resulting attention maps from IJES-OA Net are accurate enough that increase experts' reliance on the provided results.

A fully-convolutional residual encoder-decoder neural network to localize breast cancer on histopathology images.

Cancer detection in its early stages may allow patients to receive the proper treatment and save lives along with recovering the routine lifestyles. Breast cancer is of the top leading causes of mortality among women all around the globe. A source to find these cancerous nuclei is through analyzing histopathology images. These images, however, are very complex and large. Thus, locating the cancerous nuclei in them is very challenging. Hence, if an expert fails to diagnose their patients via these images, the situation may be exacerbated. Therefore, this study aims to introduce a method to mask as many cancer nuclei on histopathology images as possible with a high visual aesthetic to make them distinguishable by experts easily. A tailored residual fully convolutional encoder-decoder neural network based on end-to-end learning is proposed to issue the matter. The proposed method is evaluated quantitatively and qualitatively on ER + BCa H&E-stained dataset. The average detection accuracy achieved by the method is 98.61%, which is much better than that of competitors.