To improve the accuracy and robustness of left ventricular segmentation, This paper proposes a diffusion probabilistic model-based left ventricular segmentation network (DPM-LVSN). DPM-LVSN integrates a U-shaped encoder-decoder for feature extraction, self-attention for semantic information, and a diffusion model for noise recovery. And the efficiency is improved by using feature fusion based on Fourier transform and time step fusion based on confidence. The experimental results on EchoNet-Dynamic dataset show the effectiveness of the proposed method

In semi-supervised medical image segmentation, the use of CutMix in the Mean Teacher architecture is considered an effective strong data augmentation strategy. However, we believe that randomly selecting patches from the source image might mislead the model into learning unexpected feature representations. Therefore, we propose Gradient Saliency-aware CutMix for semi-supervised medical image segmentation (GSC-Seg). Utilizing the gradient from pre-trained models to detect salient regions and then copies and pastes the large gradient areas from labeled data into corresponding areas of unlabeled data based on the gradient, and vice versa, guiding the model to learn more appropriate feature representations. Furthermore, we propose a gradient augmentation strategy, which generates disruptions in the gradient through the network itself and enhances the gradient representation abilities of the network. Experiment results show that our approach achieves the state-of-the-art performance on three medical image segmentation datasets. Code is available at https://github.com/UESTC-Med424-JYX/GSC-Seg.

It is of the utmost importance to achieve flexible and fine-grained access control of electronic health records (EHR) in smart elderly healthcare (SEH) for providing high-quality healthcare services for the elderly and protecting their privacy simultaneously. In this paper, a flexible, fine-grained, and elderly-centric access control scheme is presented for EHR data in SEH. In the proposed scheme, Ciphertext Policy Attribute Based Encryption (CP-ABE), permission token, dual-key regression, and blockchain techniques are leveraged to realize multi-dimensional access control of EHR data in terms of data generation time, data user properties, access times, and access period. Moreover, a novel token segmentation algorithm is designed to transfer access rights between doctors efficiently for multi-party diagnosis and treatment. Since the elderly can define the attributes of users accessing his/her EHR data, the access number, the access time, and the access range of data from the time dimension of data generation with the cooperation of the Smart Elderly Healthcare (SEH) institution, the privacy of EHR data of the elderly is well protected. The security analysis demonstrates that our scheme can achieve EHR ciphertext indistinguishability under chosen-plaintext attacks and token unlinkability and unforgeability under data users’ collusion attacks. The experimental results show that our scheme performs well in terms of time cost and computational overhead.

Multiple human parsing (MHP) is typically treated as two sub-tasks, i.e., instance separation and body part segmentation. Existing methods usually tackle the sub-tasks by adopting a two-stage strategy, which regards MHP as an ROI-based (i.e., detect-then-segment) or grouping-based (i.e., segment-then-grouping) paradigm. However, the strong dependence between the two sub-tasks limits the potential of an MHP method, since it often requires qualified prior predictions. Besides, isolated models responsible for the two sub-tasks bring a significant computational burden. Unlike existing methods, we regard MHP as a hierarchical set prediction problem and handle two sub-tasks using several landmarks of body parts. Motivated by this, we propose a novel multiple human parser with representative sets, termed ReSParser. In ReSParser, several landmarks of body parts are hierarchically estimated, resulting in coarse-to-fine representative sets. After that, each representative set is adaptively responsible for segmenting pixels into semantically consistent regions belonging to the corresponding person. In such a manner, the ReSParser simultaneously addresses two sub-tasks in a fully convolutional fashion, thus eliminating the dependence between two sub-tasks and significantly alleviating computational complexity. Extensive experiments on two challenging benchmarks demonstrate that our proposed ReSParser is an efficient framework with a superior parsing performance, which significantly outperforms that of other ROI-free yet grouping-free methods. Besides, it achieves competitive results to that of the best two-stage methods such as RP-RCNN, but requires a much lower inference time, showing a good precision-speed trade-off. We hope the ReSParser serves as a new baseline for multiple human parsing research in the future.

Most existing semantic segmentation methods primarily employ supervised learning with discriminative models. Although these methods are straightforward, they overlook the modeling of underlying data distributions. In this paper, we propose a novel medical image segmentation framework called Diff-SFCT based on Diffusion Model. We formulate semantic segmentation as a generative problem for segmentation masks, replacing the conventional pixel-wise discriminative learning with a latent prior learning process to produce more accurate segmentation results. Diff-SFCT employs a backbone network combining Convolutional Neural Network (CNN) and Transformer, and utilizes the local perception of CNN and the global information modeling capability of Transformer. In Diff-SFCT, we design a Semantic Encoder that effectively extracts fine-grained semantic features from real images. Meanwhile, we propose a novel Spatial-Frequency Cross Transformer (SFCT) framework, which can effectively model and interact the global features of the diffuse noise mask and the real semantic features, reducing the domain gap between the two and enhancing the model’s representational capacity. Additionally, to preserve spatial and frequency information in the diffusion model, we design a Spatial-Frequency Attention Module (SFAM) as part of the Convolutional Block. This module improves the model’s spatial and frequency perception abilities while incurring negligible computational overhead. Experimental results evince that our DiffSFCT substantially outperforms other segmentation methods, exhibiting remarkable performance across various medical image segmentation datasets.

Path probability prediction is essential to describe the dynamic birth and death of propagation paths, and build the accurate channel model for air-to-ground (A2G) communications. The occurrence probability of each path is complex and time variant due to fast changeable altitudes of unmanned aerial vehicles and scattering environments. Considering the A2G channels under urban scenarios, this article presents three novel stochastic probability models for the Line-of-Sight (LoS) path, ground specular (GS) path, and building scattering (BS) path, respectively. By analyzing the geometric stochastic information of 3-D scattering environments, the proposed models are derived with respect to the width, height, and distribution of buildings. The effect of the Fresnel zone and altitudes of transceivers are also taken into account. Simulation results show that the proposed LoS path probability model has good performance at different frequencies and altitudes and is also consistent with existing models at the low or high altitude. Moreover, the proposed LoS and non-LoS path probability models show good agreement with the ray-tracing (RT) simulation method.

Histopathology diagnosis is an important standard for breast tumors identifying. However, histopathology image analysis is complex, tedious and error-prone, due to the super-resolution image. In recent years, deep learning technology has been successfully applied to histopathology image analysis and made great progress. The well-known deep neural networks usually have tens of million parameters, which consume much memory to deploy the state-of-the-art model. In addition, deep neural networks rely on high-performance hardware resources, which impede the deployment of state-of-the-art model on portable equipment. In this work, a novel framework which consists of a weight accumulation method and a lightweight fast neural network (FastNet) was proposed for tumor fast identification (TFI) in mobile computer-assisted devices. The weight accumulation method was designed to obtain the tissue mask regions of interest and remove the useless background area in histopathology images, which greatly reduces the redundant computation cost. Furthermore, we proposed the lightweight FastNet to improve the computational efficiency on mobile devices. A novel attention loss function was designed and applied in FastNet. The attention loss function pays more attention on the positive samples and the indistinguishable samples, which greatly improves performance. The proposed FastNet was compared with three state-of-the-art methods commonly used for image classification and object detection. Experimental results indicated that FastNet achieves highest recall of 96.94%, highest F1 score of 97.33% and highest accuracy of 97.34%, besides least trainable parameters of 0.22M and smallest floating point operations of 210M FLOPs.

Line-of-sight (LoS) probability modeling plays an essential role in the reliability determination of millimeter wave (mmWave) communication systems. However, since most LoS probability models do not consider altitude information of communication terminals, they cannot be directly applied to air-to-ground (A2G) communication scenarios. In this paper, we propose a new multi-height LoS probability model for mmWave unmanned aerial vehicle (UAV) communication scenarios. A machine learning (ML)-based parameter estimation method is also developed, which trains the data from the constructed virtual urban scenes. We first propose a LoS/none-LoS (NLoS) identification method to recognize the LoS path and calculate the LoS probability. Then, we construct a two-layer single-input multiple-output back propagation neural network (BPNN) which trains the relationship between the model parameters and the altitude of UAVs. Simulation results show that the proposed LoS probability model has a good consistency with the ray tracing (RT) simulation data and the currently existing models when altitudes of UAVs are low. As the altitude increases, our model is still applicable and can achieve an excellent agreement with the RT data.

Line-of-sight (LoS) probability prediction is critical to the performance optimization of wireless communication systems. However, it is challenging to predict the LoS probability of air-to-ground (A2G) communication scenarios, because the altitude of unmanned aerial vehicles (UAVs) or other aircraft varies from dozens of meters to several kilometers. This paper presents an altitude-dependent empirical LoS probability model for A2G scenarios. Before estimating the model parameters, we design a K -nearest neighbor (KNN) based strategy to classify LoS and non-LoS (NLoS) paths. Then, a two-layer back propagation neural network (BPNN) based parameter estimation method is developed to build the relationship between every model parameter and the UAV altitude. Simulation results show that the results obtained using our proposed model has good consistency with the ray tracing (RT) data, the measurement data, and the results obtained using the standard models. Our model can also provide wider applicable altitudes than other LoS probability models, and thus can be applied to different altitudes under various A2G scenarios.