Jue Hou

Supracondylar humerus fractures in children are among the most common elbow fractures in pediatrics. However, their diagnosis can be particularly challenging due to the anatomical characteristics and imaging features of the pediatric skeleton. In recent years, convolutional neural networks (CNNs) have achieved notable success in medical image analysis, though their performance typically relies on large-scale, high-quality labeled datasets. Unfortunately, labeled samples for pediatric supracondylar fractures are scarce and difficult to obtain. To address this issue, this paper introduces a deep learning-based multi-scale patch residual network (MPR) for the automatic detection and localization of subtle pediatric supracondylar fractures. The MPR framework combines a CNN for automatic feature extraction with a multi-scale generative adversarial network to model skeletal integrity using healthy samples. By leveraging healthy images to learn the normal skeletal distribution, the approach reduces the dependency on labeled fracture data and effectively addresses the challenges posed by limited pediatric datasets. Datasets from two different hospitals were used, with data augmentation techniques applied during both training and validation. On an independent test set, the proposed model achieves an accuracy of 90.5%, with 89% sensitivity, 92% specificity, and an F1 score of 0.906—outperforming the diagnostic accuracy of emergency medicine physicians and approaching that of pediatric radiologists. Furthermore, the model demonstrates a fast inference speed of 1.1 s per sheet, underscoring its substantial potential for clinical application.

The task of fashion parsing aims to assign pixel-level labels to clothing targets; thereby, parsing models are required to have good contextual recognition ability. However, the shapes of clothing components are complex, and the types are difficult to distinguish. Recent solutions focus on improving datasets and supplying abundant priori information, but the utilization of features by more efficient methods is rarely explored. In this paper, we propose a multi-scale fashion parsing model called the Priori Dictionary Network (PDN), which includes a priori attention module and a multi-scale backbone. The priori attention module extracts high dimensional features from our designed clothing average template as a priori information dictionary (priori dictionary, PD), and the PD is utilized to activate the feature maps of a CNN from a multi-scale attention mechanism. The backbone is derived from classical models, and five side paths are designed to leverage the richer features of local and global contextual representations. To measure the performance of our method, we evaluated the model on four public datasets, the CFPD, UTFR-SBD3, ModaNet and LIP, and the experimental results show that our model stands out from other State of the Art in all four datasets. This method can assist with the labeling problem of clothing datasets.

Background Labeled fracture radiographs are usually difficult to acquire, especially for the small sample sizes of the supracondylar fractures for children. Convolutional neural network-based methods, which rely heavily on a large amount of labeled data, cannot yield satisfactory performance. Compared to the fracture data, normal radiographs without the need for annotation are much easier to capture and include many images. Methods In this study, we propose a subtle supracondylar fracture detection framework, called the multiscale patch residual (MPR), which can learn the bone characteristics from normal data to repair fracture areas and forms residual features with the original image for fracture location. Our proposed MPR framework is a repair-based method that can learn the distribution of normal data by removing occlusions. A multiscale generation adversarial model is proposed for learning the bone consistency features from normal data. For the repaired image to be more similar to the real data, edges and textures are added as auxiliary information. Furthermore, weighted-binary cross-entropy (W-BCE) is used in our detection model to further enhance the fracture detection accuracy by adjusting the difference between the fracture area and the nonfracture area and forcing the model to learn the feature changes before and after repair. Additional experiments are conducted on the cross time independent test set, and a comparative experiment was conducted with an emergency specialist and a pediatric radiologist. The experimental results confirm the effectiveness of our approach. Results The final accuracy of independent test set was 93.5%, the sensitivity was 89%, the specificity was 98%, and the F1 value was 0.932. The accuracy of emergency physicians was 81%, the sensitivity was 62%, the specificity was 42%, and the F1 value was 0.62. The accuracy of children's radiologists was 93%, the sensitivity was 92%, the specificity was 94.2%, and the F1 value was 0.929. Conclusions Our method has achieved a good diagnostic rate, far exceeding that of emergency doctors and reaching the same level as pediatric radiologists.

Particulate matter (PM) and airborne viruses bring adverse influence on human health. As the most feasible way to prevent inhalation of these pollutants, face masks with excellent filtration efficiency and low press drop are in urgent demand. In this study, we report a novel methodology for producing high performance air filter by combining melt blown technique with corona charging treatment. Changing the crystal structure of polypropylene by adding magnesium stearate can avoid charge escape and ensure the stability of filtration performances. Particularly, the influence of fiber diameter, pore size, porosity, and charge storage on the filtration performances of the filter are thoroughly investigated. The filtration performances of the materials, including the loading test performance are also studied. The melt blown materials formed by four layers presented a significant filtration efficiency of 97.96%, a low pressure drop of 84.28 Pa, and a high quality factor (QF) of 0.046 Pa⁻¹ for paraffin oil aerosol particles. Meanwhile, a robust filtration efficiency of 99.03%, a low pressure drop of 82.32 Pa, and an excellent QF of 0.056 Pa⁻¹ for NaCl aerosol particles could be easily achieved. The multi-layered melt blown filtration material developed here would be potentially applied in the field of protective masks.

This paper presents a new method for extracting 3D point clouds from multi-focus images of a fibrous web acquired on an optical microscope to analyze microscopic structures of a fibrous web. The algorithm consists of two major parts: (1) utilizing a convolutional neural network (CNN) to extract in-focus objects from multi-focus images, and (2) a depth identification module (DIM) which is a frequency domain-based model used to identify the depths of object points. The network, namely the multi-focus image deblurring network (MIDN), was designed by introducing gradient features into the network to deblur images and generate the ranges of focal depths of object points. Based on the results of MIDN, DIM was constructed to calculates the focal plane depth for each point. The experiments show that the combination of MIDN and DIM provides a practical way to generate complete, accurate 3D structures of nonwoven.