An Algorithm for Automatic Rib Fracture Recognition Combined with nnU-Net and DenseNet.

Rib fracture is the most common thoracic clinical trauma. Most patients have multiple different types of rib fracture regions, so accurate and rapid identification of all trauma regions is crucial for the treatment of rib fracture patients. In this study, a two-stage rib fracture recognition model based on nnU-Net is proposed. First, a deep learning segmentation model is trained to generate candidate rib fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to determine whether they are fractures or not. The results show that the two-stage deep learning model proposed in this study improves the accuracy of rib fracture recognition and reduces the false-positive and false-negative rates of rib fracture detection, which can better assist doctors in fracture region recognition.

PCXRNet: Pneumonia Diagnosis From Chest X-Ray Images Using Condense Attention Block and Multiconvolution Attention Block.

Coronavirus disease2019 (COVID-19)has become a global pandemic. Many recognition approaches based on convolutional neural networks have been proposed for COVID-19 chest X-ray images. However, only a few of them make good use of the potential inter- and intra-relationships of feature maps. Considering the limitation mentioned above, this paper proposes an attention-based convolutional neural network, called PCXRNet, for diagnosis of pneumonia using chest X-ray images. To utilize the information from the channels of the feature maps, we added a novel condense attention module (CDSE) that comprised of two steps: condensation step and squeeze-excitation step. Unlike traditional channel attention modules, CDSE first downsamples the feature map channel by channel to condense the information, followed by the squeeze-excitation step, in which the channel weights are calculated. To make the model pay more attention to informative spatial parts in every feature map, we proposed a multi-convolution spatial attention module (MCSA). It reduces the number of parameters and introduces more nonlinearity. The CDSE and MCSA complement each other in series to tackle the problem of redundancy in feature maps and provide useful information from and between feature maps. We used the ChestXRay2017 dataset to explore the internal structure of PCXRNet, and the proposed network was applied to COVID-19 diagnosis. As a result, the network achieves an accuracy of 94.619%, recall of 94.753%, precision of 95.286%, and F1-score of 94.996% on the COVID-19 dataset.

A multi-feature image retrieval scheme for pulmonary nodule diagnosis.

Deep analysis of radiographic images can quantify the extent of intra-tumoral heterogeneity for personalized medicine.In this paper, we propose a novel content-based multi-feature image retrieval (CBMFIR) scheme to discriminate pulmonary nodules benign or malignant. Two types of features are applied to represent the pulmonary nodules. With each type of features, a single-feature distance metric model is proposed to measure the similarity of pulmonary nodules. And then, multiple single-feature distance metric models learned from different types of features are combined to a multi-feature distance metric model. Finally, the learned multi-feature distance metric is used to construct a content-based image retrieval (CBIR) scheme to assist the doctors in diagnosis of pulmonary nodules. The classification accuracy and retrieval accuracy are used to evaluate the performance of the scheme.The classification accuracy is 0.955 ±â€Š0.010, and the retrieval accuracies outperform the comparison methods.The proposed CBMFIR scheme is effective in diagnosis of pulmonary nodules. Our method can better integrate multiple types of features from pulmonary nodules.

[Research progress on fabric electrode technologies for electrocardiogram signal acquisition].

In recent years, wearable devices grew up gradually and developed increasingly. Aiming at the problems of skin sensibility and the change of electrode impedance of Ag/AgCl electrode in the process of long-term electrocardiogram (ECG) signal monitoring and acquisition, this paper discussed in detail a new sensor technology-fabric electrode, which is used for ECG signal acquisition. First, the concept and advantages of fabric electrode were introduced, and then the common substrate materials and conductive materials for fabric electrode were discussed and evaluated. Next, we analyzed the advantages and disadvantages from the aspect of textile structure, putting forward the evaluation system of fabric electrode. Finally, the deficiencies of fabric electrode were analyzed, and the development prospects and directions were prospected.