Haoran Wang

Distributed optical acoustic sensing (DAS) based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) can realize the distributed monitoring of multi-point disturbances along the optical fiber, which has wide-ranging applications in various fields. However, owing to the fading phenomenon in the DAS, the signal strength is greatly attenuated, thereby causing the phase signal to jump and complicating the continuous and effective collection of the disturbance signal. Considering the amplitude, we propose a fading adaptive mask and masked autoencoders (FAM-MAE) algorithm for fading discrimination and suppression of DAS system. The differential calculation and analysis of the amplitude signal are performed, and the non-differentiable point is the fading point. The density-based spatial clustering of applications with noise (DBSCAN) is used to determine the position of the phase fading point. The masked autoencoders (MAE) were used to reconstruct these positions to achieve fading suppression. The experimental results indicate that the average signal-to-noise ratio (SNR) of the vibration signal of piezoelectric transducer (PZT) is improved from 4.81 to 21.92 dB after fading suppression. The SNR of the underwater acoustic disturbance signal is improved by 12.68 dB after fading suppression. Therefore, the proposed method can suppress the signal distortion caused by fading and recover the disturbance signal completely.

Diabetic retinopathy (DR) is a common complication of diabetes and one of the main causes of blindness in humans, which can be prevented by early-stage detection and treatment. Clinically, ophthalmologists use optical coherence tomography (OCT) image analysis as a basis for diagnosing DR. The existing medical resources can no longer meet the needs of the escalating patient population. Therefore, deep learning technology has become a mainstream solution for medical image analysis. Vision Transformer (ViT), a new neural network structure, has demonstrated great performance in analyzing images. However, due to the lack of inductive bias and prohibition of input image changes in size, ViT cannot avoid over-fitting problems on small datasets and limits the model to biological tissue characteristics. Thus, we propose an OCT multi-head self-attention (OMHSA) block that especially calculates OCT image information based on a hybrid CNN-Transformer strategy. Compared to traditional MHSA, OMHSA integrates local information extraction differences into the calculation of self-attention and adds local information to the transformer model without relying on a multi-branch network establishment. We built a neural network architecture (OCT-Former) by stacking convolutional layers and OMHSA blocks repeatedly in each stage. Similar to CNN, OCTFormer allows input size change at each stage to achieve a hierarchical structure effect. The model diagnosis effectiveness on the collected retinal OCT dataset was evaluated, and the accuracy reached 98.60%, surpassing the state-of-the-art (SOTA) model. The OCTFormer deployment to mobile terminals through knowledge distillation technology was shown, which presented a reference for deploying transformer models to actual clinical environments.

Genitourinary syndrome of menopause (GSM) is a group of syndromes, including atrophy of the reproductive tract and urinary tract, and sexual dysfunction, caused by decreased levels of hormones, such as estrogen, in women during the transition to, or late stage of, menopause. GSM symptoms can gradually become severe with age and menopausal time, seriously affecting the safety, and physical and mental health, of patients. Optical coherence tomography (OCT) systems can obtain images similar to “optical slices” in a non-destructive manner. This paper presents a neural network, called RVM-GSM, to implement automatic classification tasks for different types of GSM-OCT images. The RVM-GSM module uses a convolutional neural network (CNN) and a vision transformer (ViT) to capture local and global features of the GSM-OCT images, respectively, and, then, fuses the two features in a multi-layer perception module to classify the image. In accordance with the practical needs of clinical work, lightweight post-processing is added to the final surface of the RVM-GSM module to compress the module. Experimental results showed that the accuracy rate of RVM-GSM in the GSM-OCT image classification task was 98.2%. This result is better than those of the CNN and Vit models, demonstrating the promise and potential of the application of RVM-GSM in the physical health and hygiene fields for women.

Optical Coherence Tomography (OCT) technology is essential to obtain glaucoma diagnostic data non-invasively and rapidly. Early diagnosis of glaucoma can be achieved by analyzing the thickness and shape of retinal layers. Accurate retinal layer segmentation assists ophthalmologists in improving the efficiency of disease diagnosis. Deep learning technology is one of the most effective methods for processing OCT retinal layer images, which can segment different retinal layers and effectively obtain the topological structure of the boundary. This paper proposes a neural network method for retinal layer segmentation based on the CSWin Transformer (CTS-Net), which can achieve pixel-level segmentation and obtain smooth boundaries. A Dice loss function based on boundary areas (BADice Loss) is proposed to make CTS-Net learn more features of edge regions and improve the accuracy of boundary segmentation. We applied the model to the publicly available dataset of glaucoma retina, and the test results showed that mean absolute distance (MAD), root mean square error (RMSE), and dice-similarity coefficient (DSC) metrics were 1.79 pixels, 2.15 pixels, and 92.79%, respectively, which are better than those of the compared model. In the cross-validation experiment, the ranges of MAD, RMSE, and DSC are 0.05 pixels, 0.03 pixels, and 0.33%, respectively, with a slight difference, which further verifies the generalization ability of CTS-Net.

Lung cancer is one of the most common malignant tumors in human beings. It is highly fatal, as its early symptoms are not obvious. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the final diagnosis of many diseases. Therefore, pathology diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far outpace the number of pathologists, especially for the treatment of lung cancer in less developed countries. To address this problem, we propose a plug-and-play visual activation function (AF), CroReLU, based on a priori knowledge of pathology, which makes it possible to use deep learning models for precision medicine. To the best of our knowledge, this work is the first to optimize deep learning models for pathology image diagnosis from the perspective of AFs. By adopting a unique crossover window design for the activation layer of the neural network, CroReLU is equipped with the ability to model spatial information and capture histological morphological features of lung cancer such as papillary, micropapillary, and tubular alveoli. To test the effectiveness of this design, 776 lung cancer pathology images were collected as experimental data. When CroReLU was inserted into the SeNet network (SeNet_CroReLU), the diagnostic accuracy reached 98.33%, which was significantly better than that of common neural network models at this stage. The generalization ability of the proposed method was validated on the LC25000 dataset with completely different data distribution and recognition tasks in the face of practical clinical needs. The experimental results show that CroReLU has the ability to recognize inter- and intra-class differences in cancer pathology images, and that the recognition accuracy exceeds the extant research work on the complex design of network layers.

Genitourinary syndrome of menopause (GSM) is a disease caused by a physiological decline in estrogen levels, and it can negatively affect a woman’s overall health and quality of life in terms of sexual function. Real-time optical biopsy images can now be obtained with optical coherence tomography (OCT) systems. In this study, we introduce vision transformer (ViT) to the field of medical OCT images for the first time and propose a deep learning-based approach for GSM lesion screening. Specifically, we first build a GSM dataset to train and evaluate the experimental model performance. The study aims to propose a method that combines null convolution with a deep convolutional adversarial generative network classifier to generate the samples needed for training to alleviate the hindrance of such problems, in response to certain practical problems, such as category imbalance that occur during data collection. Next, the experiments present ViT PLUS (ViT-P) for the vaginal OCT image classification task used, which effectively improves the shortcomings of ViT in extracting Patch Embedding using a multibranch convolutional neural network combined with a channel attention mechanism. The clinical images acquired by the OCT device are then automatically classified on the basis of the OCT device to reduce the medical workload of gynecologists. Experimental results show that the ViT-P model outperforms the CNN model and ViT for case screening in the GSM and UCSD datasets, and the accuracy can reach 99.9% and 99.69%, respectively.

Real-time identification of threats has been the focus of attention in the security field in recent years. Researchers have aimed to build a fast, efficient, and accurate model to address risks in threat event identification. This study proposes a fast, effective deep learning model-the squeeze and excitation WaveNet (SE-WaveNet). WaveNet is a 1-D convolutional neural network (CNN) model with a large receptive field. It is small, fast, and highly accurate. Thus, it has a great advantage in processing time series data. On this basis, the squeeze and excitation (SE) structure of the attention mechanism is introduced to adapt to matrix data with complete information. To ensure the integrity of information in the experimental verification, the distributed optical fiber vibration sensing (DVS) system is used to collect the data near the vibration point and form the time-space matrix as the input of the model. SE-WaveNet is compared with WaveNet and related models. Results show that the accuracy of SE-WaveNet based on WaveNet is increased by approximately 3.5%, the model size is reduced by 39%, and the speed is increased by 32%. Compared with the five other related models, the average performance is 3%-9% higher, and the final test accuracy rate can reach approximately 97.73%.