SST: a snore shifted-window transformer method for potential obstructive sleep apnea patient diagnosis

Objective.Obstructive sleep apnea (OSA) is a high-incidence disease that is seriously harmful and potentially dangerous. The objective of this study was to develop a noncontact sleep audio signal-based method for diagnosing potential OSA patients, aiming to provide a more convenient diagnostic approach compared to the traditional polysomnography (PSG) testing.Approach.The study employed a shifted window transformer model to detect snoring audio signals from whole-night sleep audio. First, a snoring detection model was trained on large-scale audio datasets. Subsequently, the deep feature statistical metrics of the detected snore audio were used to train a random forest classifier for OSA patient diagnosis.Main results.Using a self-collected dataset of 305 potential OSA patients, the proposed snore shifted-window transformer method (SST) achieved an accuracy of 85.9%, a sensitivity of 85.3%, and a precision of 85.6% in OSA patient classification. These values surpassed the state-of-the-art method by 9.7%, 10.7%, and 7.9%, respectively.Significance.The experimental results demonstrated that SST significantly improved the noncontact audio-based OSA diagnosis performance. The study's findings suggest a promising self-diagnosis method for potential OSA patients, potentially reducing the need for invasive and inconvenient diagnostic procedures.

CPFTransformer: transformer fusion context pyramid medical image segmentation network

Introduction

The application of U-shaped convolutional neural network (CNN) methods in medical image segmentation tasks has yielded impressive results. However, this structure's single-level context information extraction capability can lead to problems such as boundary blurring, so it needs to be improved. Additionally, the convolution operation's inherent locality restricts its ability to capture global and long-distance semantic information interactions effectively. Conversely, the transformer model excels at capturing global information.

Methods

Given these considerations, this paper presents a transformer fusion context pyramid medical image segmentation network (CPFTransformer). The CPFTransformer utilizes the Swin Transformer to integrate edge perception for segmentation edges. To effectively fuse global and multi-scale context information, we introduce an Edge-Aware module based on a context pyramid, which specifically emphasizes local features like edges and corners. Our approach employs a layered Swin Transformer with a shifted window mechanism as an encoder to extract contextual features. A decoder based on a symmetric Swin Transformer is employed for upsampling operations, thereby restoring the resolution of feature maps. The encoder and decoder are connected by an Edge-Aware module for the extraction of local features such as edges and corners.

Results

Experimental evaluations on the Synapse multi-organ segmentation task and the ACDC dataset demonstrate the effectiveness of our method, yielding a segmentation accuracy of 79.87% (DSC) and 20.83% (HD) in the Synapse multi-organ segmentation task.

Discussion

The method proposed in this paper, which combines the context pyramid mechanism and Transformer, enables fast and accurate automatic segmentation of medical images, thereby significantly enhancing the precision and reliability of medical diagnosis. Furthermore, the approach presented in this study can potentially be extended to image segmentation of other organs in the future.

Multi-Level Cycle-Consistent Adversarial Networks with Attention Mechanism for Face Sketch-Photo Synthesis

The synthesis between face sketches and face photos has important application values in law enforcement and digital entertainment. In cases of a lack of paired sketch-photo data, this paper proposes an unsupervised model to solve the problems of missing key facial details and a lack of realism in the synthesized images of existing methods. The model is built on the CycleGAN architecture. To retain more semantic information in the target domain, a multi-scale feature extraction module is inserted before the generator. In addition, the convolutional block attention module is introduced into the generator to enhance the ability of the model to extract important feature information. Via CBAM, the model improves the quality of the converted image and reduces the artifacts caused by image background interference. Next, in order to preserve more identity information in the generated photo, this paper constructs the multi-level cycle consistency loss function. Qualitative experiments on CUFS and CUFSF public datasets show that the facial details and edge structures synthesized by our model are clearer and more realistic. Meanwhile the performance indexes of structural similarity and peak signal-to-noise ratio in quantitative experiments are also significantly improved compared with other methods.

Few-shot learning with deformable convolution for multiscale lesion detection in mammography

PURPOSE

Image-based breast lesion detection is a powerful clinical diagnosis technology. In recent years, deep learning architectures have achieved considerable success in medical image analysis however, they always require large-scale samples. In mammography images, breast lesions are inconspicuous, multiscale, and have blurred edges. Moreover, few well-labeled images exist. Because of these factors, the detection accuracy of conventional deep learning methods is low. Therefore, we attempted to improve the accuracy of mammary lesion detection by introducing transfer learning (TL) into a deep learning framework for the few-shot learning task and thus provide a method that will further assist physicians in detecting breast lesions.

METHODS

In this paper, we propose a method called "few-shot learning with deformable convolution for multiscale lesion detection in mammography," named FDMNet. Deformable convolution is introduced for enhancing the network's ability to detect lesions, and the sensitivity of the multiscale feature space is reinforced by using a feature pyramid method. Furthermore, by introducing location information in the predictor, the sensitivity of the model to lesion location is also enhanced. The proposed method, through the TL technique that is applied mines the potentially common knowledge of features in the source domain and transfers it into the target domain to improve the accuracy of breast lesion detection in the few-shot learning task.

RESULTS

On the publicly available datasets for screening mammography CBIS-DDSM and Mini-MIAS, the proposed method performs better than five widely used detection methods. On the CBIS-DDSM dataset, its comprehensive scores, sensitivity, precision, and the mean dice similarity coefficient are 0.911, 0.949, 0.873, and 0.913, respectively, and on the Mini-MIAS dataset, these values are 0.931, 0.966, 0.882, and 0.941, respectively.

CONCLUSIONS

To achieve the few-shot learning required for medical image analysis, the proposed method uses TL to execute feature knowledge transformation and includes deformable convolution to build a feature pyramid structure, which enhances the learning performance of the network for lesions. The results of comparative numerical experiments show that the proposed method outperforms some state-of-the-art methods.