Contrast-free myocardial infarction (MI) segmentation is essential for mitigating the health risks associated with contrast agents (CAs) in clinical diagnostics. However, existing approaches are limited by their reliance on strictly paired CINE sequences and contrast-enhanced images, which are often difficult to obtain because patient conditions and imaging protocols often cause inter-modality slice misalignments. Therefore, we propose MTMS, the first label-free training and contrast-free MI segmentation model, enabling effective training without requiring paired datasets. Notably, MTMS is the first framework to incorporate cardiac biomechanical knowledge into contrast-free MI segmentation through a self-supervised paradigm. It leverages dual upstream guidance, combining pseudo-label generation from biomechanical cues with structural for segmentation, and achieves self-supervised learning via iterative pseudo-label refinement. MTMS includes three synergistic modules, Upstream 1: Spatiotemporal Structural Evolution Module that encodes myocardial structure transitions by guided-perturbation modeling of inter-frame morphological divergence, enabling explicit extraction of deformation trajectories critical for infarct localization; Upstream 2: Cardiac Mechanics-Driven Analysis Module that estimates myocardial stress responses by diffeomorphic motion fields and strain energy formulation, enabling generation of physiologically consistent pseudo-labels that reflect regional mechanical dysfunction; Downstream: Dual-Domain Interaction Module that combines structural and biomechanical cues by prototype-guided semantic fusion, enabling consistent and physiologically grounded delineation of infarct boundaries. On 370 clinical cases, MTMS achieves a Dice of 0.698 and HD95 of 19.634, surpassing seven state-of-the-art methods by up to 0.30 in Dice and over 107.392 in HD95. These results demonstrate the potential of MTMS to advance the development of contrast-free MI segmentation. Code is available at https://github.com/wrsssss/mtms.

Federated learning has emerged as a transformative paradigm for distributed data collaboration, facilitating knowledge aggregation across multiple local clients through a global server while rigorously preserving data privacy. However, its performance is significantly hindered by the global head-tail imbalance, where tail classes with scarce data are often dominated by head classes. This challenge, known as federated long-tailed learning, arises from the intrinsic conflict between class knowledge acquisition and privacy preservation. Existing methodologies falter in resolving this conflict, as the abstraction of data knowledge in federated communication complicates the extraction of class-level knowledge, resulting in imbalanced global models and diminished performance. To simultaneously address this imbalance and uphold privacy, we introduce FedGRE, a gradient-refined federated learning approach that constructs global gradients and facilitates refined global gradient descent. FedGRE enhances gradients through two pivotal mechanisms: accumulation diffusion and accumulation refinement. The former amalgamates accumulated gradients with stochastic gradient perturbations to alleviate class imbalance, while the latter utilizes the accumulation as an anchor to calibrate global gradient updates, ensuring consistency and mitigating oscillations. Additionally, we implement a consistency integration technique to incorporate the refined accumulation into the global model, guaranteeing privacy-preserving and class-balanced global optimization. Extensive experiments on six datasets demonstrate that FedGRE significantly outperforms 14 state-of-the-art (SOTA) methods in federated long-tailed classification while maintaining robust privacy protection.

With the proliferation of social media, assessing depression risk through user-generated posts has emerged as a critical and prominent research direction. However, existing methods fail to capture clinically relevant linguistic markers due to ungrounded semantic representations in non-expert models. Therefore, this paper proposes a model, ProDoKE, which leverages prompt chaining integrated with domain knowledge to facilitate in-depth extraction and interpretation of clinically significant linguistic and semantic cues from social media posts. ProDoKE comprises two key modules, consisting of a prompt chaining enhancing module and a capsule fusion module. Following the collection of risky posts, the prompt chaining module systematically integrates domain-specific knowledge with Large Language Models (LLMs). This structured reasoning process guides the LLM to identify relevant clinical symptoms and then infer their underlying emotional states, resulting in a richer and more accurate interpretation. Furthermore, the capsule fusion module incorporates contrastive learning to optimize the alignment of emotional and symptom semantics, while using capsule networks to fortify the model's robustness. Evaluations on the eRisk2017 and eRisk2018 datasets show that ProDoKE achieves state-of-the-art performance, significantly outperforming previous baseline models in F1 scores.

ABSTRACT Recent studies have shown that deep neural networks are vulnerable to adversarial attacks. In the field of 3D point cloud classification, transfer‐based black‐box attack strategies have been explored to address the challenge of limited knowledge about the model in practical scenarios. However, existing approaches typically rely excessively on network structure, resulting in poor transferability of the generated adversarial examples. To address the above problem, the authors propose AEattack , an adversarial attack method capable of generating highly transferable adversarial examples. Specifically, AEattack employs an autoencoder (AE) to extract features from the point cloud data and reconstruct the adversarial point cloud based on these features. Notably, the AE does not require pre‐training, and its parameters are jointly optimised using a loss function during the process of generating adversarial point clouds. The method makes the generated adversarial point cloud not overly dependent on the network structure, but more concerned with the data distribution. Moreover, this design endows AEattack with a broader potential for application. Extensive experiments on the ModelNet40 dataset show that AEattack is capable of generating highly transferable adversarial point clouds, with up to 61.8% improvement in transferability compared to state‐of‐the‐art adversarial attacks.

Synthesis of myocardial infarction enhancement (MIE) images without contrast agents (CAs) has shown great potential to advance myocardial infarction (MI) diagnosis and treatment. It provides results comparable to late gadolinium enhancement (LGE) images, thereby reducing the risks associated with CAs and streamlining clinical workflows. The existing knowledge-and-data-driven approach has made progress in addressing the complex challenges of synthesizing MIE images (i.e., invisible myocardial scars and high inter-individual variability) but still has limitations in the interpretability of kinematic inference, morphological knowledge integration, and kinematic-morphological fusion, thereby reducing the transparency and reliability of the model and causing information loss during synthesis. In this paper, we proposed a knowledge-driven interpretative conditional diffusion model (K-ICDM), which learns kinematic and morphological information from non-enhanced cardiac MR images (CINE sequence and T1 sequence) guided by cardiac knowledge, enabling the synthesis of MIE images. Importantly, our K-ICDM introduces three key innovations that address these limitations, thereby providing interpretability and improving synthesis quality. (1) A novel cardiac causal intervention that generates counterfactual strain to intervene in the inference process from motion maps to abnormal myocardial information, thereby establishing an explicit relationship and providing the clear causal interpretability. (2) A knowledge-driven cognitive combination strategy that utilizes cardiac signal topology knowledge to analyze T1 signal variations, enabling the model to understand how to learn morphological features, thus providing interpretability for morphology capture. (3) An information-specific adaptive fusion strategy that integrates kinematic and morphological information into the conditioning input of the diffusion model based on their specific contributions and adaptively learns their interactions, thereby preserving more detailed information. Experiments on a broad MI dataset with 315 patients show that our K-ICDM achieves state-of-the-art performance in contrast-free MIE image synthesis, improving structural similarity index measure (SSIM) by at least 2.1% over recent methods. These results demonstrate that our method effectively overcomes the limitations of existing methods in capturing the complex relationship between myocardial motion and scar distribution and integrating of static and dynamic sequences, thus enabling the accurate synthesis of subtle scar boundaries.

Domain generalization aims to learn common knowledge from multiple observed source domains and transfer it to unseen target domains, e.g. the object recognition in varieties of visual environments. Traditional domain generalization methods aim to learn the feature representation of the raw data with its distribution invariant across domains. This relies on the assumption that the two posterior distributions (the distributions of the label given the feature distribution and given the raw data) are stable in different domains. However, this does not always hold in many practical situations. In this paper, we relax the above assumption by permitting the posterior distribution of the label given the raw data changes in difference domains, and thus focuses on a more realistic learning problem that infers the conditional domain-invariant feature representation. Specifically, a multi-domain adversarial variational Bayesian inference approach is proposed to minimize the inter-domain discrepancy of the conditional distributions of the feature given the label. Besides, it is imposed by the constraints from the adversarial learning and feedback mechanism to enhance the condition invariant feature representation. The extensive experiments on two datasets demonstrate the effectiveness of our approach, as well as the state-of-the-art performance comparing with thirteen methods.

All three contrast-enhanced (CE) phases (e.g., Arterial, Portal Venous, and Delay) are crucial for diagnosing liver tumors. However, acquiring all three phases is constrained due to contrast agents (CAs) risks, long imaging time, and strict imaging criteria. In this paper, we propose a novel Common-Unique Decomposition Driven Diffusion Model (CUDD-DM), capable of converting any two input phases in three phases into the remaining one, thereby reducing patient wait time, conserving medical resources, and reducing the use of CAs. 1) The Common-Unique Feature Decomposition Module, by utilizing spectral decomposition to capture both common and unique features among different inputs, not only learns correlations in highly similar areas between two input phases but also learns differences in different areas, thereby laying a foundation for the synthesis of remaining phase. 2) The Multi-scale Temporal Reset Gates Module, by bidirectional comparing lesions in current and multiple historical slices, maximizes reliance on previous slices when no lesions and minimizes this reliance when lesions are present, thereby preventing interference between consecutive slices. 3) The Diffusion Model-Driven Lesion Detail Synthesis Module, by employing a continuous and progressive generation process, accurately captures detailed features between data distributions, thereby avoiding the loss of detail caused by traditional methods (e.g., GAN) that overfocus on global distributions. Extensive experiments on a generalized CE liver tumor dataset have demonstrated that our CUDD-DM achieves state-of-the-art performance (improved the SSIM by at least 2.2% (lesions area 5.3%) comparing the seven leading methods). These results demonstrate that CUDD-DM advances CE liver tumor imaging technology.

Glomerulus detection is a critical component of renal histopathology assessment, essential for diagnosing glomerulonephritis. To mitigate the increasing workload on pathologists, AI-assisted diagnostic methods based on high-resolution digital pathology whole slide images have been developed. However, these current AI-assisted approaches are limited to high-resolution whole slide images, necessitating expensive digital scanner equipment, high image storage costs, and significant computational complexity. To address this limitation, this paper pioneers a method for facilitating glomerulus detection in low-resolution human kidney pathology images. Specifically, we propose a novel multi-step hybrid knowledge distillation method. Our method distills both the global features and the semantic information through a hybrid knowledge distillation strategy that integrates offline and online knowledge distillation, where the information from high-resolution pathological images is successively transferred to student model from the global features in the shallow network layers to the semantic information of the back-end through a multi-step training strategy. Experimental results on two datasets show that the proposed method achieves effective detection outcomes for low-resolution kidney pathology images. Compared to other state-of-the-art detection techniques, our method achieves an ${AP}_{0.5:0.95}$ improvement of 23.1% on the private LN dataset and 15.9% on the public HUBMAP dataset.

3D point cloud classifiers exhibit vulnerability to imperceptible perturbations, which poses a serious threat to the security and reliability of deep learning models in practical applications, making the robustness evaluation of deep 3D point cloud models increasingly important. Due to the difficulty in obtaining model parameters, black-box attacks have become a mainstream means of assessing the adversarial robustness of 3D classification models. The core of improving the transferability of adversarial examples generated by black-box attacks is to generate better generalized adversarial examples, where data augmentation has become one of the popular approaches. In this article, we employ five mainstream attack methods and combine six data augmentation strategies, namely point dropping, flipping, rotating, scaling, shearing, and translating, in order to comprehensively explore the impact of these strategies on the transferability of adversarial examples. Our research reveals that data augmentation methods generally improve the transferability of the adversarial examples, and the effect is better when the methods are stacked. The interaction between data augmentation methods, model characteristics, attack, and defense strategies collectively determines the transferability of adversarial examples. In order to comprehensively understand and improve the effectiveness of adversarial examples, it is necessary to comprehensively consider these complex interrelationships.

Deep reinforcement learning (DRL) has demonstrated impressive performance in medical image segmentation, particularly for low-contrast and small medical objects. However, current DRL-based segmentation methods face limitations due to the optimization of error propagation in two separate stages and the need for a significant amount of labeled data. In this paper, we propose a novel deep generative adversarial reinforcement learning (DGARL) approach that, for the first time, enables end-to-end semi-supervised medical image segmentation in the DRL domain. DGARL ingeniously establishes a pipeline that integrates DRL and generative adversarial networks (GANs) to optimize both detection and segmentation tasks holistically while mutually enhancing each other. Specifically, DGARL introduces two innovative components to facilitate this integration in semi-supervised settings. First, a task-joint GAN with two discriminators links the detection results to the GAN's segmentation performance evaluation, allowing simultaneous joint evaluation and feedback. This ensures that DRL and GAN can be directly optimized based on each other's results. Second, a bidirectional exploration DRL integrates backward exploration and forward exploration to ensure the DRL agent explores the correct direction when forward exploration is disabled due to lack of explicit rewards. This mitigates the issue of unlabeled data being unable to provide rewards and rendering DRL unexplorable. Comprehensive experiments on three generalization datasets, comprising a total of 640 patients, demonstrate that our novel DGARL achieves 85.02% Dice and improves at least 1.91% for brain tumors, achieves 73.18% Dice and improves at least 4.28% for liver tumors, and achieves 70.85% Dice and improves at least 2.73% for pancreas compared to the ten most recent advanced methods, our results attest to the superiority of DGARL. Code is available at GitHub.

Mammography screening is one of the important applications for the intelligent Internet of Things (IoT). Due to the efficient and personalized cyber-medicine system, early diagnosis can successfully reduce the breast cancer mortality rate by AI-driven healthcare. However, it is a huge challenge to extend the conventional single-center into the multicenter mammography screening, thus improving the effectiveness and robustness of intelligent IoT-based devices. To address this problem, we utilize multicenter mammograms by the modified capsule neural network and propose a novel framework called multicenter transformation between unified capsules (MLT-UniCaps) in this article. The proposed MLT-UniCaps is composed of Attentional Pose Embedding, Dynamic Source Capsule Traversal, and Adaptive Target Capsule Fusion to realize an intelligent remote assistant diagnosis. Attentional Pose Embedding extracts feature vectors via variations in position, orientation, scale, and lighting as the poses through an adversarial convolutional neural network with an attention-based layer. Based on the pose presentation, Dynamic Source Capsule Traversal deploys a dynamic routing mechanism between neurons to build a source cancer classifier for single-center mammography screening. Using the source cancer classifier, Adaptive Target Capsule Fusion integrates various centers of mammograms as the universal cancer detectors and optimizes heterogeneous distribution among them by the transformation-likelihood maximization. Owing to the three components, MLT-UniCaps effectively improves the results of single-center mammography screening and works in the multicenter breast cancer diagnosis. By comprehensive experiments on 58 965 samples, the proposed MLT-UniCaps obtains 90.1% of overall classification accuracy on single-center trials and 73.8% of overall F1 score on multicenter trials. All the experimental results illustrated that our MLT-UniCaps, an intelligent IoT-based clinical tool, inures the benefit of mammography screening.

3D semi-supervised medical image segmentation is extremely essential in computer-aided diagnosis, which can reduce the time-consuming task of performing annotation. The challenges with current 3D semi-supervised segmentation algorithms includes the methods, limited attention to volume-wise context information, their inability to generate accurate pseudo labels and a failure to capture important details during data augmentation. This article proposes a dual uncertainty-guided mixing consistency network for accurate 3D semi-supervised segmentation, which can solve the above challenges. The proposed network consists of a Contrastive Training Module which improves the quality of augmented images by retaining the invariance of data augmentation between original data and their augmentations. The Dual Uncertainty Strategy calculates dual uncertainty between two different models to select a more confident area for subsequent segmentation. The Mixing Volume Consistency Module that guides the consistency between mixing before and after segmentation for final segmentation, uses dual uncertainty and can fully learn volume-wise context information. Results from evaluative experiments on brain tumor and left atrial segmentation shows that the proposed method outperforms state-of-the-art 3D semi-supervised methods as confirmed by quantitative and qualitative analysis on datasets. This effectively demonstrates that this study has the potential to become a medical tool for accurate segmentation. Code is available at:https://github.com/yang6277/DUMC.

Automatic segmentation of myocardial infarction (MI) regions in late gadolinium-enhanced cardiac magnetic resonance images is an essential step in the computed diagnosis of myocardial infarction. Most of the current myocardial infarction region segmentation methods are based on fully supervised deep learning. However, cardiologists' annotation of myocardial infarction regions in cardiac magnetic resonance images during the diagnosis process is time-consuming and expensive. This paper proposes a semi-supervised myocardial infarction segmentation. It consists of two models: 1) a boundary mining model and 2) an adversarial learning model. The boundary mining model can solve the boundary ambiguity problem by enlarging the gap between the foreground and background features, thus segmenting the myocardial infarction region accurately. The adversarial learning model can make the boundary mining model learn from additional unlabeled data by evaluating the segmentation performance and providing pseudo supervision, which significantly increases the robustness of the boundary mining model. We conduct extensive experiments on an in-house myocardial magnetic resonance dataset. The experimental results on six evaluation metrics demonstrate that our method achieves excellent results in myocardial infarction segmentation and outperforms the state-of-the-art semi-supervised methods.

Cross-modality data processing is critical for the Internet-of-Things (IoT) deployment in healthcare. It can convert the innumerable raw day-to-day medical big data from massive IoT-based medical devices to diagnostic valuable data so that they can be feed to clinical routine. In this article, we propose a novel spatiotemporal two-streams generative adversarial network (SpGAN) as a cross-modality data processing approach to deploy the medical IoT in infarction learning. Our SpGAN remotely converts diagnostic valuable contrast-enhanced images (the “gold standard” for infarction learning, but it requires the injection of contrast agents) directly from raw nonenhanced cine MR images. This converting allows physicians to remotely perform infarction observation and analysis to break through the limitations of time and space by building a cloud computing platform of IoT-based MRI devices. Importantly, this converting offers a low-risk IoT-based manner to eliminate the potential fatal risk caused by contrast agent injection in the current infarction learning workflow. Specifically, SpGAN consists of: 1) a spatiotemporal two-stream framework as an encoding–decoding model to achieve data converting and 2) a spatiotemporal pyramid network enhances those features that are responsible to the infarction learning during encoding to improve decoding performance. Real IoT-based remote diagnosis experiments performed on 230 patients demonstrate that SpGAN provides high-quality converted images for infarction learning and promotes the in-depth application and deployment of IoT in the medical field.

Numerous surveillance data processing is crucial in the Internet-of-Things systems with pervasive edge computing. In this process, salient object detection from surveillance videos plays an important role because it provides the human-concerned semantic cue for various industrial tasks. However, it is still challenging for the existing studies with two aspects. The first one is the redundant saliency information from moving background to disturb the detection of salient objects. The second one is the difficulty to model the spatiotemporal saliency uncertainty. To overcome these challenges. In this article, an intelligent approach is proposed for surveillance saliency detection. It enables a region-proposal-based optical flow strategy to suppress the saliency enhancement of non-salient regions due to the moving background. Besides, it develops the bidirectional Bayesian state transition strategy to model the motion uncertainty for refining the spatiotemporal saliency feature. Extensive experiments have been performed on two datasets (the increase of Fβis larger than 0.01 for DAVIS, and larger than 0.015 for UVSD), and the comparison with seven methods to evaluate the effectiveness of the proposed approach.

Trustful Internet of Things (IoT) plays an important role in smart cities. The trust information in surveillance data motivates the analysis of images from numerous IoT devices. Saliency detection is a fundamental step in surveillance data analysis for providing help to the subsequent tasks, but unsuitable to IoT applications owing to the neglect of image similarity and difference from diverse IoT devices. To solve this problem, we enable the co-saliency detection in IoT, which detects the common and salient foreground regions in the group surveillance images. The main contributions include: 1) enable a multistage context perception scheme to efficiently extract the contextual information corresponding to different-size receptive fields in the single image; 2) construct a two-path information propagation to extract the interimage similarity and difference from the high-level image feature representations of the group images; and 3) propose the stage-wise refinement to allocate the label information to different parts of the network for helping the network to learn the enriched semantically common knowledge. The extensive experiments performed on three public data sets can demonstrate the effectiveness of our approach and its superiority to four state-of-the-art co-saliency detection methods.

Segmentation of carotid intima-media (IM) borders from ultrasound sequences is challenging because of unknown image noise and varying IM border morphologies and/or dynamics. In this paper, we have developed a state-space framework to sequentially segment the carotid IM borders in each image throughout the cardiac cycle. In this framework, an ${\mathrm{H}}_{\mathrm{\infty }}$ filter is used to solve the state-space equations, and a grayscale-derivative constraint snake is used to provide accurate measurements for the ${\mathrm{H}}_{\mathrm{\infty }}$ filter. We have evaluated the performance of our approach by comparing our segmentation results to the manually traced contours of ultrasound image sequences of three synthetic models and 156 real subjects from four medical centers. The results show that our method has a small segmentation error (lumen intima, LI: 53 $\pm\, 67\;{\mathrm{\mu }}$m; media-adventitia, MA: 57 $\pm\, 63\;{\mathrm{\mu }}$m) for synthetic and real sequences of different image characteristics, and also agrees well with the manual segmentation (LI: bias = 1.44 ${\mathrm{\mu }}$m; MA: bias = $-$3.38 ${\mathrm{\mu }}$m). Our approach can robustly segment the carotid ultrasound sequences with various IM border morphologies, dynamics, and unknown image noise. These results indicate the potential of our framework to segment IM borders for clinical diagnosis.