Predicting the risk of subsequent miscarriage during the first trimester is crucial for optimizing ultrasound surveillance and alleviating psychological distress for pregnant women. This study aims to develop a multi-input convolutional neural network (CNN) that integrates early gestational ultrasound images with clinical measurements to assess miscarriage risk, while providing interpretable visual explanations to support clinical decision-making. Utilizing a retrospective dataset of singleton pregnancies at 6-8 weeks of gestation, we extracted gestational sac regions through three distinct segmentation strategies and trained a CNN-based classifier to differentiate between normal and miscarriage outcomes. The predictive performance was evaluated using accuracy, precision, recall, f1-score, and the area under the receiver operating characteristic curve (AUC). The results indicated that the multi-input CNN outperformed single-input models. Additionally, an edge-expanded segmentation strategy demonstrated superior performance compared to both precise segmentation and rectangular cropping. By incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP), we identified subchorionic regions as significant biomarkers and clarified the differences in attention to features among different models, thereby enhancing the interpretability and clinical applicability of the model for early pregnancy risk assessment.

The combination of positron emission tomography (PET) and computed tomography (CT) can accurately reflect the metabolic and anatomical information of a variety of tumors, including nasopharyngeal carcinoma, lymphoma and lung cancer, which plays an important role in the diagnosis, staging and efficacy evaluation of tumors. Accurate and automatic delineation of target tumors is crucial for radiotherapy, however, the tumor segmentation task is extremely challenging due to the fuzzy tumor boundaries, uncertain locations, and the scattered distribution of multiple tumors throughout the body. To this end, this study extended the STUNet pre-trained on the TotalSegmentator dataset and proposed M-AECA, which integrates a Mamba-based auxiliary encoder (M-AE) to provide multi-scale global features for enhanced feature extraction. In addition, an Inter-Branch Feature Fusion Module (IBFFM) is designed to achieve more comprehensive global and local feature fusion through cross attention (CA) and feature subspace projection. The method was evaluated on Hecktor and AutoPET datasets, demonstrating superior performance compared to other comparison methods. In the test sets of these two datasets, the average Dice similarity coefficients of the proposed method were 70.86% and 64.91%, respectively. In addition, the results of the ablation experiment show that the proposed M-AE and IBFFM demonstrate strong performance and significant advantages.

Telemedicine Monitoring (TM) integrates mobile communication technology and Internet of Things (IoT) technology for health monitoring and data management. Amidst the escalating demand for telemedicine, traditional cloud computing struggles to guarantee real-time performance and data privacy. To address these challenges, we systematically survey the application of fog and edge computing technologies in TM systems. We focus on the following key aspects: (1) We delve into the theoretical foundations of fog and edge computing, underscoring their salient advantages including low latency, location awareness, high mobility, and more. (2) We elaborate on the architecture of a TM system hinged on fog and edge computing. (3) We outline key challenges facing fog/edge computing-based TM systems, including bandwidth limitations, low latency, data security, privacy, heterogeneity, and reliability. (4) We discuss the need for future advancements in the realms of security defense capability, system adaptability, and convergence of scheduling algorithms to refine the construction of the TM system and stimulate the development of telemedicine.

Finger motion tracking is a significant challenge in the field of motion capture. However, existing technology for finger motion tracking often requires the wearing of a heavy device and a laborious calibration process to track the bending angle of each joint; this can be challenging, particularly because the motion of each finger has a high coupling characteristic. To address this issue, in this work, we have proposed a compressed sensing-based tracking (CST) framework that enables the estimation of the bending angle of all hand joints using sensors smaller than the number of hand joints. Our framework also integrates a real-time calibration function, which significantly simplifies the calibration process. We developed a glove with multiple liquid metal sensors and an inertial measurement unit to evaluate the effectiveness of our CST framework. The experimental results show that our CST framework can achieve high-speed and accurate hand arbitrary motion capture with only 12 sensors. The motion-tracking gloves developed on this basis are user-friendly and particularly suitable for human–computer interaction applications in robot control, the metaverse and other fields.

Nonnegative matrix factorization (NMF) has been widely used to learn low-dimensional representations of data. However, NMF pays the same attention to all attributes of a data point, which inevitably leads to inaccurate representations. For example, in a human-face dataset, if an image contains a hat on a head, the hat should be removed or the importance of its corresponding attributes should be decreased during matrix factorization. This article proposes a new type of NMF called entropy weighted NMF (EWNMF), which uses an optimizable weight for each attribute of each data point to emphasize their importance. This process is achieved by adding an entropy regularizer to the cost function and then using the Lagrange multiplier method to solve the problem. Experimental results with several datasets demonstrate the feasibility and effectiveness of the proposed method. The code developed in this study is available at https://github.com/Poisson-EM/Entropy-weighted-NMF.

Inspired by complexity and diversity of biological neurons, our group proposed quadratic neurons by replacing the inner product in current artificial neurons with a quadratic operation on input data, thereby enhancing the capability of an individual neuron. Along this direction, we are motivated to evaluate the power of quadratic neurons in popular network architectures, simulating human-like learning in the form of "quadratic-neuron-based deep learning". Our prior theoretical studies have shown important merits of quadratic neurons and networks in representation, efficiency, and interpretability. In this paper, we use quadratic neurons to construct an encoder-decoder structure, referred as the quadratic autoencoder, and apply it to low-dose CT denoising. The experimental results on the Mayo low-dose CT dataset demonstrate the utility and robustness of quadratic autoencoder in terms of image denoising and model efficiency. To our best knowledge, this is the first time that the deep learning approach is implemented with a new type of neurons and demonstrates a significant potential in the medical imaging field.

We provide a general form for many reconstruction estimators of emission tomography. These estimators include Shepp and Vardi's maximum likelihood (ML) estimator, the quadratic weighted least squares (WLS) estimator, Anderson's WLS estimator, and Liu and Wang's multi-objective estimator, and others. We derive a generic update rule by constructing a surrogate function. This work is inspired by the ML-EM (EM, expectation maximization), where the latter naturally arises as a special case. A regularization with a specific form can also be incorporated by De Pierro's trick. We provide a general and quite different convergence proof compared with the proofs of the ML-EM and De Pierro. Theoretical analysis shows that the proposed algorithm monotonically decreases the cost function and automatically meets nonnegativity constraints. We have introduced a mechanism to provide monotonic, self-constraining, and convergent algorithms, from which some interesting existing and new algorithms can be derived. Simulation results illustrate the behavior of these algorithms in term of image quality and resolution-noise tradeoff.