Interactive medical image segmentation plays a crucial role in medical diagnosis, treatment planning, and in-terventional procedures. With the rapid advancement of artificial intelligence and deep learning technologies, this field has not only seen significant improvements in the accuracy and efficiency of segmenting anatomical structures and pathological areas from medical images, but it has also made substantial strides in the development of user-centric interactive tools. The introduction of the Segmentation Anything Model (SAM) represents a significant expansion of prompt-driven approaches within the domain of image segmentation, introducing a plethora of previously untapped functionalities. However, due to the substantial differences between natural and medical images, the generalization capabilities of interactive segmentation models across different imaging techniques remain limited, necessitating the ongoing development of targeted interactive segmentation models. In this work, we provide a comprehensive overview aimed at extending the efficacy of SAM to various medical imaging modalities, encompassing diverse image analyses and domains. Additionally, we explore potential research directions for SAM in different medical imaging fields. Despite these advancements, the accuracy and robustness of segmentation models still largely depend on high-quality annotated data, which is costly and time-consuming to acquire. These research efforts herald significant forthcoming advancements in interactive medical image segmen-tation technology, with the potential to greatly improve patient outcomes and streamline medical procedures, thereby driving further development and innovation in the healthcare industry.

Accurate and reproducible assessment of myocardial conditions is essential for the diagnosis of prior infarctions, cardiomyopathies, and inflammatory diseases. Although cardiac magnetic resonance imaging (CMR) is regarded as the gold standard for evaluating myocardial anatomy and function, manual segmentation remains labor-intensive and susceptible to variability. The demand for high-resolution images, dense predictions, and comprehensive segmentation across all phases of the cardiac cycle introduces significant challenges for Cine-CMR analysis. To address these issues, we present EML-SAM, an innovative interactive segmentation model based on the Segment Anything Model (SAM). EML-SAM incorporates an early-mid-late (EML) multi-scale fusion strategy, effectively mitigating the premature dilution of interaction information, thereby reducing segmentation errors and enhancing feature utilization. Furthermore, the model utilizes a multi-scale linear attention (MLA) transformer to establish an efficient global receptive field. Comprising 12 Transformer layers, the model integrates multi-scale attention with multi-scale perceptrons, striking a balance between computational efficiency and capacity. The proposed methodology offers a robust and efficient solution for high-resolution Cine-CMR segmentation, delivering accurate and real-time segmentation performance throughout the various stages of temporal changes within the cardiac cycle.

Fabric defect detection is critical for quality control in the textile industry. However, current challenges include the scarcity of high-quality datasets, the difficulty of detecting small defects in complex environments, and suboptimal detection performance for elongated defects. To address these issues, we propose an improved Real-Time Detection Transformer (RT-DETR) model specifically designed for fabric defect detection in industrial settings. A custom fiber defect dataset was created with random augmentations for improved diversity. We designed the Global Attention-based Instance Feature Interaction (GA-AIFI) module to enhance local feature extraction, improving the model's detection capability for small defects. Additionally, to address the prevalence of elongated defects in industrial fabric production, we introduced the Aspect Ratio-aware Distance-IoU (AR-DIoU) loss function, which further improves bounding box localization accuracy. Experiments conducted on both the custom dataset and the MVTec dataset demonstrate significant improvements, with [email protected] increasing by 3.5% and 3.6%,respectively, confirming the model's effectiveness.

Detection of wood surface defects is one critical step in the plywood process. YOLO series detection models need to find a good tradeoff between detection speed and accuracy. Therefore, this paper improves the YOLOv8 model by replacing the backbone, neck, and head with the FasterNet, Enhanced Feature Pyramid Network (EFPN), and Multi-Scale Feature Decoupling Head (MSDH), respectively. The FasterNet reduces the number of model parameters by using Partial Convolution (PConv). To remedy the accuracy loss caused by the FasterNet, the EFPN adds auxiliary branches to reduce information loss between different feature layers and uses Dilated Reparameter Modules (DRM) to extract more multi-scale features. Besides, the MSDH reorganizes the features on different scales to enhance their semantic relationships. Experimental results show that FasterNet reduces the number of parameters by 43%, the EFPN and the MSDH increase the mAP (mean Average Precison) by 2.2% and 1.4%, respectively, compared to the original Yolov8 model.

Field-road mode mining (FRMM) has gained increasing attention because of its crucial role in machinery management. As the Global Navigation Satellite System (GNSS) is widely applied in agricultural machinery, many methods based on motion and spatial-temporal features of GNSS trajectory data have been proposed for FRMM. However, these methods ignore the density and parallel features of GNSS points. The two features are useful for FRMM because field points usually have a higher density and more parallel points than road points. Therefore, a neural network driven by density and parallel features is proposed for accurate FRMM. Firstly, a statistical method is designed to extract the density feature (i.e., the number of its neighbor points) and parallel feature (i.e., the number of approximately parallel points in its neighbors) of each point. Then, the two features and eight motion features (e.g., speed, direction, etc.) are fed into a neural network to extract valuable latent features. Finally, a linear classifier is used to identify the field and road categories of GNSS points based on the latent features. Experimental results show that our method outperforms state-of-the-art methods and achieves the accuracy of 91.69% and 86.44% on public Wheat and Paddy datasets, respectively.

For the Dynamic Flexible Job Shop Scheduling Problem (DFJSP), this study proposes an Improved Artificial Lemming Algorithm with integrated Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to solve the bi-objective optimization problem of minimizing makespan and maximizing machine utilization, with a focus on addressing scheduling optimization challenges under dynamic disturbances such as sudden machine failures. This work pioneers the application of the Artificial Lemming Algorithm (ALA) to Flexible Job Shop Scheduling (FJSP),proposing an efficient improved version called Improved Non-dominated Sorting Artificial Lemming Algorithm(INALA).Differential evolution (DE) strategies are embedded in both the global exploration and local exploitation phases of ALA, enhancing the algorithm’s search capability while improving the convergence accuracy of solution sets.A dynamic probability-controlled variable neighborhood search(VNS) perturbation mechanism is designed to enhance solution diversity and local optimization efficiency. Additionally, this paper proposes a sectional crossover mechanism based on precedence preserving order-based crossover (POX) and multi-point replacement crossover, which can effectively handle dynamic events such as machine failures. Finally, the effectiveness and practicality of the algorithm are verified through case studies.

In the fault diagnosis of industrial equipment, transfer learning alleviates the problem of data distribution offset and annotation scarcity through cross domain knowledge migration. However, the existing methods have limitations. Single scale feature alignment ignores the difference between shallow and deep features. The pseudo label strategy with fixed temperature parameters reduces the feature discrimination, and the single distribution alignment is difficult to take into account the global and local structure. Therefore, this paper proposes a multi-scale dynamic confrontation transfer learning framework (MDATL), which includes a hierarchical dynamic confrontation mechanism, and dynamically adjusts the characteristic confrontation intensity of each layer through periodic GRL; the two-stage pseudo label optimization strategy combined with temperature scaling softens the probability distribution of the target domain; a mixed distribution alignment strategy, which combines MMD and CORAL to dynamically balance global statistics and local covariance. Experiments using CWRU and PU data sets of six groups of cross condition task verification show the superior performance of this method in unsupervised fault diagnosis.

Abstract Parkinson's disease (PD) is a neurodegenerative disorder primarily affecting middle‐aged and elderly populations. Its insidious onset, high disability rate, long diagnostic cycle, and high diagnostic costs impose a heavy burden on patients and their families. Leveraging artificial intelligence, with its rapid diagnostic speed, high accuracy, and fatigue resistance, to achieve intelligent assisted diagnosis of PD holds significant promise for alleviating patients' financial stress, reducing diagnostic cycles, and helping patients seize the golden period for early treatment. This paper proposes an Attention and Multi‐level Enhancement Fusion Network (AMEF‐Net) based on the characteristics of three‐dimensional medical imaging and the specific manifestations of PD in medical images. The focus is on small lesion areas and structural lesion areas that are often overlooked in traditional deep learning models, achieving multi‐level attention and processing of imaging information. The model achieved a diagnostic accuracy of 98.867%, a precision of 99.830%, a sensitivity of 99.182%, and a specificity of 99.384% on Magnetic Resonance Images from the Parkinson's Progression Markers Initiative dataset. On Diffusion Tensor Images, it achieved a diagnostic accuracy of 99.602%, a precision of 99.930%, a sensitivity of 99.463%, and a specificity of 99.877%. The relevant code has been placed in https://github.com/EdwardTj/AMEF‐NET .

Cervical liquid-based cell pathology image detection and recognition is a key task in gynecological pathology aimed at early detection of cervical precancerous lesions. In this study, a deep learning based method is proposed for the detection and recognition of cervical liquid-based cell images. This paper is based on the YOLOv5 network model for improvement. Firstly, in the data preprocessing stage: for the characteristics of the dataset, this paper proposes two data enhancement methods to augment the training dataset; secondly, the Transformer module for local information enhancement is improved by introducing a local attention mechanism, which restricts each position to only pay attention to positions within a certain range around it; then, the spatial pyramid pooling module (DSPPCSPC) is improved, and the spatial Pyramid Pooling Module (DSPPC-SPC), introducing deformable mask of deformable convolution (ODConv); then, this paper proposes a new Attention Module (XCAttention), improving S2Attention to focus on the local information of the image, and introducing its Channel Attention Module into the third branch, which performs the Dot with the first and the second branches that have gone through Spatial-shift to obtain the final output. Product operation to get the final output. Through experiments, this paper’s algorithm improves in Precision, Recall, mAP50, and mAP50-95 metrics by 1.9%, 1.9%, 2%, and 1.2%, respectively. The experimental results show that this paper’s algorithm can effectively improve the detection performance of the model.

Federated learning (FL) is a distributed machine learning framework enabling multiple clients to collaboratively train a shared ML model without sharing raw data. Despite its aim to safeguard data security and privacy, FL faces risks from advanced adversarial attacks like membership inference attack (MIA), potentially leaking sensitive information. To counter these threats, differential privacy (DP) methods add noise to shared model parameters. However, traditional DP methods struggle with balancing model accuracy, training efficiency, and privacy protection, often compromising one for the other. Additionally, uniform DP mechanisms may not cater to varying client privacy and performance needs. To tackle these challenges, we propose a federated learning framework based on localized random differential privacy (FedLRDP). This approach empowers each client to control noise levels based on their privacy requirements, enhancing model performance while preserving data privacy. We further optimize client-side loss functions to enhance model performance. Theoretical analysis establishes the convergence bounds for FedLRDP, demonstrating its superior convergence performance. Experimental results on MNIST reveal that FedLRDP improves model accuracy by 3.28% compared to traditional DP methods while mitigating MIA with a 72.48% lower attack success rate than FedAvg. Moreover, on EMNIST, FedLRDP boosts model accuracy by 7.31% compared to traditional DP methods, maintaining a 59.4% lower attack success rate than FedAvg. These findings underscore FedLRDP’s efficacy in meeting client privacy needs while enhancing model performance.