The incidence of spinal diseases has been steadily increasing in recent years, with affected populations becoming progressively younger. With the rapid development of artificial intelligence and medical image processing technologies, automated intelligent diagnostic algorithms based on spinal X-ray images have been continuously developed. However, current deep learning–based diagnostic methods for spinal diseases face limitations such as the limited availability of labeled data and subtle inter-class feature differences. To address these challenges in spinal X-ray image diagnosis, this study proposes a novel Siamese Swin Transformer model based on transfer learning, within the Internet of Medical Things (IoMT) framework—referred to as the SpineSiamSwin model. The model adopts a Siamese network structure and leverages metric learning loss functions to maximize the distance between different classes in the embedding space, thereby enhancing the model’s sensitivity to subtle structural differences between disease categories. Extensive experiments on real-world spinal X-ray datasets demonstrate the effectiveness and practical applicability of the SpineSiamSwin model.

In recent years, skeleton-based action recognition has made significant progress but still faces several pressing challenges. Notably, the motion field ranges of different actions vary significantly, and existing methods struggle to simultaneously capture macromorphology and microdetail features of actions. This limitation impedes the accurate modeling of action-specific patterns. To address this issue, we propose a novel Motion Field Adaptive Network (MFA-Net). This network employs a parallel dual-stream architecture, comprising a Macromorphology Field Perception Stream (MmFPS) and a Microdetail Field Perception Stream (MdFPS) respectively. Specifically, MmFPS introduces a spatio-temporal collaborative perception mechanism to model global dependencies in macroscopic posture evolution, capturing the complete semantics and dynamic trends of actions. Meanwhile, MdFPS contains layer-wise modulated spatial graph convolution and multi-scale temporal field convolution, enhancing the representation of local spatio-temporal features. Additionally, we present a Motion Field Adaptive Fusion (MFAF) strategy to fully leverage the advantages of both streams while allowing the model to flexibly adjust the relative weights of macromorphology and microdetail features based on the motion field ranges of actions, thus achieving more precise action recognition. Experimental results on three representative benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.

Multimodal sentiment analysis uses text, visual, and audio data to assess user sentiment, while both the discrimination power of modalities and sample distributions over categories remain imbalanced in practice. To address these challenges, we propose a Multi-grained Perception Model with Perceived Contrastive Focal loss, denoted MGSA1. More specifically, we design a Multi-grained Cross-modal Attention Perception (MCP) module, which employs coarse-grained and fine-grained cross-modal attention to deeply explore the complementary semantics between modalities, thereby modeling sentiment polarity and intensity by fusing text-video and text-audio data, respectively. Modeling sentiment polarity and intensity helps alleviate feature interference between modalities due to their differing discriminative power. Furthermore, the Perceived Contrastive Focal (PCF) loss is designed to address the challenge of unbalanced samples. We enhance the focal loss by incorporating inverse document frequency to dynamically weight samples within each class. Furthermore, information noise contrastive estimation is introduced to replace the class probability predictions in the enhanced focal loss, thereby more effective differentiation between positive and negative samples. Experiments on the MOSI and MOSEI datasets demonstrate that MGSA outperforms all baselines across a range of metrics.

Social media serves as a real-time collecting and disseminating center of users’ ideas, opinions, and experiences. The deliberate disinformation and rumors propagate rapidly online due to their exaggerated facts, controversial opinions, divisive perspectives, and stunning expressions. Rumor detection approaches typically use social media posts with rumor or non-rumor labels for training and testing without disclosing the rationale behind decision-makings. On one hand, collecting evidence data to verify claims relies on expert efforts. On the other hand, verifying the truthfulness of confusing claims with distracting and lengthy evidences is still challenging. In this paper, we contribute a rumor detection dataset with multi-granularity evidences, denoted as the RD-E dataset, which includes response, fact-check, article, sourcing data and generated evidence by large language models, supporting models to verify the truthfulness of claims on social media. A number of 32,892 claims from 4,525 public individuals and organizations are annotated to 6 kinds of labels, including true, mostly true, half true, mostly false, false, pants on fire, covering a wide range of topics, e.g., politics, economy, society, technology, and health. In the experiments, seven rumor detection models have been investigated and customized on four predefined subtasks for comparisons.

Social media allows the public to access information conveniently, in which the false messages that are eye-catching may spread fast. In this paper, we propose a two-stage confidence-guided boundary adaption (CBA) network, consisting of a feature preprocessing (FP) module, a biased ambiguity learning (BA) module and a confidence-guided boundary adaptation (CG) module. In the first stage, the FP module obtains the textual and visual features, which are fused by conducting the visual-to-textual and textual-to-visual correlation coefficients with attention mechanism. Furthermore, BA evaluates the distribution distance between fused features and single modalities to determine the weights between modalities, capturing the semantics of key modality. In the second stage, CG leverages samples from the low-confidence interval to generate new instances using a mixup of augmentation techniques, aiming to occupy the decision space and optimize the decision boundary of the classifier. Extensive experiments on two public datasets show that our CBA model is 1.6% and 2.6% higher than the state-of-the-art methods.