Semi-supervised medical image segmentation network based on mutual learning.

The detection of coreferent pairs within a text is one of the basic tasks in the area of natural language processing (NLP). The state‑ of‑ the‑ art methods of coreference resolution are based on machine learning algorithms. The key idea of the methods is to detect certain regularities between the semantic or grammatical features of text entities. In the paper, the comparative analysis of current methods of coreference resolution in English and Ukrainian texts has been performed. The key disadvantage of many methods consists in the interpretation of coreference resolution as a classification problem. The result of coreferent pairs detection is the set of groups in which elements refer to a common entity. Therefore it is advisable to consider the coreference resolution as a clusterization task. The method of coreference resolution using the set of filtering sieves and a convolutional neural network has been suggested. The set of filtering sieves to find candidates for coreferent pairs formation has been implemented. The training process of a multichannel convolutional neural network on a marked Ukrainian corpus has been performed. The usage of a multichannel structure allows analyzing of the different components of text units: semantic, lexical, and grammatical features of words and sentences. Furthermore, it is possible to process input data with unfixed size (words or sentences of a text) using a convolutional layer. The output result of the method is the set of clusters. In order to form clusters, it is necessary to take into account the previous steps of the model’s workflow. Nevertheless, such an approach contradicts the traditional methodology of machine learning. Thus, the training process of the network has been performed using the SEARN algorithm that allows the solving of tasks with unfixed output structures using a classifier model. An experimental examination of the method on the corpus of Ukrainian news has been performed. In order to estimate the accuracy of the method the corresponding common metrics for clusterization tasks have been calculated. The results obtained can indicate that the suggested method can be used to find coreferent pairs within Ukrainian texts. The method can be also easily adapted and applied to other natural languages.

For patients with acute ischemic stroke (AIS), rapid and accurate lesion localization is critical for improving treatment outcomes. However, automatic stroke lesion segmentation remains highly challenging due to the scarcity of large-scale annotated datasets. Recently, semi-supervised learning (SSL) has achieved remarkable progress in medical image segmentation, yet its performance is still hindered by unreliable pseudo-labels. To address this issue, we propose a novel SSL framework, termed reliability-aware mutual learning (RAML), which employs two subnetworks with a shared encoder, a primary decoder, and an auxiliary decoder. Specifically, RAML introduces uncertain region relearning (URR) regularization, which leverages prediction uncertainty from both subnetworks to identify and refine unreliable regions in labeled images. For unlabeled images, reliability-aware mutual pseudo-supervision (RMPS) regularization is designed to enable cross-supervision based on reliable pseudo-labels. Furthermore, feature difference learning (FDL) regularization is incorporated to promote prediction diversity across subnetworks. Experiments on two acute ischemic stroke datasets and the Left Atrium dataset demonstrate the effectiveness of the proposed RAML in semi-supervised segmentation tasks. The code for this project is available at https://github.com/EricMedimuist/RAML.

This paper presents a new hybrid metaheuristic model in order to estimate wind speeds accurately. The study was started by the training process of artificial neural networks with some metaheuristic algorithms such as evolutionary strategy, genetic algorithm, ant colony optimization, probability-based incremental learning, particle swarm optimization, and radial movement optimization in the literature. The success of each model is recorded in graphs. In order to make the closest estimation and to increase the system stability, a new hybrid metaheuristic model was developed using particle swarm optimization and radial movement optimization, and the training process of artificial neural networks was performed with this new model. The data were obtained by real-time measurements from a 63-m-high wind measurement station built at the coordinates of UTM E 263254 and N 4173479, altitude 1313 m. Two different scenarios were created using actual data and applied to all models. It was observed that the error values in the designed new hybrid metaheuristic model were lower than those of the other models.

Numerous transformer-based medical image segmentation methods have been proposed and achieved good segmentation results. However, it is still a challenge to train and deploy transformer networks to mobile medical devices due to a large number of model parameters. To resolve the training and model parameter problems, in this paper, we propose a Transformer-based network for Medical Image Segmentation using Knowledge Distillation named MISTKD. The MISTKD consists of a teacher network and a student network. It achieves comparable performance to state-of-the-art transformer works using fewer parameters by employing the teacher network to train the student network. The training can be implemented by extracting the sequence in the teacher and student encoder networks during the training procedure. The losses between sequences are further calculated, thus the student network can learn from the teacher network. The experimental results on Synapse show that the proposed work achieves competitive performance using only one-eighth parameters.

DSEU-net: A novel deep supervision SEU-net for medical ultrasound image segmentation

Semi-supervised learning (SSL) is an effective method for medical image segmentation as it alleviates the dependence on clinical pixel-level annotations. Among the SSL methods, pseudo-labels and consistency regularization play a key role as the dominant paradigm. However, current consistency regularization methods based on shared encoder structures are prone to trap the model in cognitive bias, which impairs the segmentation performance. Furthermore, traditional fixed-threshold-based pseudo-label selection methods lack the utilization of low-confidence pixels, making the model’s initial segmentation capability insufficient, especially for confusing regions. To this end, we propose a multi-perspective dynamic consistency (MPDC) framework to mitigate model cognitive bias and to fully utilize the low-confidence pixels. Specially, we propose a novel multi-perspective collaborative learning strategy that encourages the sub-branch networks to learn discriminative features from multiple perspectives, thus avoiding the problem of model cognitive bias and enhancing boundary perception. In addition, we further employ a dynamic decoupling consistency scheme to fully utilize low-confidence pixels. By dynamically adjusting the threshold, more pseudo-labels are involved in the early stages of training. Extensive experiments on several challenging medical image segmentation datasets show that our method achieves state-of-the-art performance, especially on boundaries, with significant improvements.

Bias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments. This study investigates fairness-aware medical image segmentation for hip and knee radiographs using deep learning models evaluated in both centralized and Federated Learning (FL) settings. We introduce Curriculum Learning (CL) strategies and Progressive Loss (PL) functions to regulate sample difficulty during training. In addition, we propose two novel fairness-oriented federated learning algorithms, Federated Intersection over Union (FedIoU) and Federated Intersection over Union with Outlier Analysis (FedIoUoutlier). Experiments are conducted using multiple segmentation backbones and simulated multi-site data partitions derived from the Osteoarthritis Initiative dataset. Model performance is evaluated using Intersection over Union (IoU), IoU standard deviation, Skewed Error Ratio (SER), and Min-Max Disparity across race and gender subgroups. Statistical significance was verified using paired t-tests to compare per-sample IoU performance against baseline configurations. Across both hip and knee segmentation tasks, curriculum learning and progressive loss strategies consistently improved segmentation accuracy and reduced demographic performance disparities in centralized training. In federated settings, fairness-aware aggregation further enhanced performance. Notably, FedIoUoutlier combined with balanced curriculum learning and tiered progressive loss achieved the highest mean IoU while yielding the lowest SER and Min-Max Disparity, indicating improved fairness without sacrificing accuracy. In several configurations, federated models matched or exceeded the performance of optimized centralized models, with statistically significant improvements in per-sample IoU over baseline configurations. The results demonstrate that structured training strategies and fairness-aware federated aggregation can jointly improve accuracy, stability, and demographic fairness in medical image segmentation. By integrating curriculum learning, progressive loss, and novel FL algorithms, this work provides a practical pathway toward equitable and privacy-preserving AI systems for medical imaging.

Environmental sound intelligence detection is one of the important means of smart city construction. The performance of the detection is particularly affected by environmental noise. To increase the performance of environmental sound (ES) recognition, different target features and masks are used in denoising, which has large differences in numerical range. However, as the number of targets with large numerical differences increases, the difficulty of network learning data rules will increase, and so will the target estimation error. The multi-objective learning noise suppression algorithm put forth in this paper aims to reduce the influence, based on a convolutional and dual temporal convolutional neural network (CNN-2TCN). The algorithm combines CNN and two parallel TCN modules to learn two types of targets with large differences in numerical range, which can optimize the training process of neural networks. In addition, the secondary targets and the IRM-based adaptive post-processing method are adopted to improve the prediction accuracy of primary targets and the quality of the reconstructed ES. The experimental results show that the proposed algorithm achieves better performance than other algorithms. Moreover, the proposed algorithm can improve the performance of the ES detection system. Especially, the ES detection system, which has used the proposed method, has made an increase of 5.95%, 31.35%, and 36.75% in the recognition accuracy under the three signal-to-noise ratios (SNR) of 5 dB, 0 dB, and -5 dB, respectively.

Neural networks have emerged as a cornerstone in the field of machine learning, driving significant advancements across various domains such as computer vision, natural language processing, and autonomous systems. This paper explores the fundamental principles of neural networks, including their structural design, activation functions, and training algorithms. Key architectures such as feedforward neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks are discussed in detail, highlighting their unique capabilities and applications. The training process of neural networks, involving techniques like backpropagation and gradient descent, is examined alongside methods to enhance performance and prevent overfitting, such as regularization and optimization strategies. This paper also reviews major applications of neural networks, showcasing their impact on image and speech recognition, language translation, and autonomous vehicles. Recent advancements in the field, including the rise of deep learning, improved model explainability, and the development of specialized hardware accelerators, are analyzed to provide insights into current trends and future prospects. The ongoing research in areas like unsupervised learning, few-shot learning, and the integration of neural networks with other AI paradigms is highlighted as a promising avenue for further innovation. By providing a comprehensive overview of neural networks and their applications, this paper underscores their transformative role in advancing machine learning technologies and anticipates future developments that will continue to shape the landscape of artificial intelligence.

In recent years, with the great success of dendritic neuron model (DNM) in various fields, the application of intelligent optimization algorithms in dendritic neuron model has attracted increasing attention of researchers. The training process of neural network is regarded as one of the great challenges of machine learning because of its non-linear nature and unknown optimal parameters. The traditional training algorithm of DNM is prone to fall into local optimum and speed of convergence slowly and so on, resulting in the problem of accuracy and low efficiency. In this paper, for solving the classification problem of dendritic neural model, an innovative intelligent optimization algorithm which named whale optimization algorithm (WOA), is applied to the training of DNM for the first time. Compared with six traditional and classic intelligent optimization algorithms in four classic datasets, the results indicate that WOA-DNM has good performance in various aspects, and its advantage is remarkable.

Because it is so difficult to distinguish handwritten digits, digit identification is one of the most critical applications in computer vision. This is one of the reasons why it is so tough. The field of handwritten character recognition is one in which a great deal of application of numerous deep learning models has occurred. The startling parallels that can be drawn between deep learning and the brain are primarily responsible for its meteoric rise in popularity. In this study, the Artificial Neural Network and the Convolutional Neural Network, two of the most used Deep Learning algorithms, were investigated with an eye toward the recognition process's feature extraction and classification phases. With the assistance of the categorical cross-entropy loss and the ADAM optimizer, the models were trained on the MNIST dataset. Backpropagation and gradient descent are the two methods utilized during the training process of neural networks that contain reLU activations and carry out automatic feature extraction. In computer vision, one of the most common and widely used classifiers is the Convolution Neural Network, sometimes referred to as ConvNets or Convolutional neural networks. This network is used for the recognition and categorization of images.

This paper presents a new method to calculate the geometric dilution of precision (GDOP) of GPS by incorporating the concept of model predictive filtering in the training process of neural networks to learn the relationship between GDOP and the azimuth and elevation of satellite. This method overcomes the shortcomings of the traditional back propagation neural networks, such as the slow convergence speed and easily falling into local minimum. A model predictive filtering algorithm is developed by using network weights as system state variables to optimize the network weights based on the neural network’s error correction. During the training process, the neural network model error is corrected by compensating the deviation between the actual and target output via the model predictive filtering. Experimental results and comparison analysis demonstrate that the proposed method can effectively approximate GDOP with improved accuracy and reduced training time.

Medical imaging technologies, as essential tools for the precise visualization of internal anatomical structures, play a crucial role in early disease detection and ensuring accurate diagnosis. Recently, semi-supervised learning has become a key strategy in medical image segmentation to reduce reliance on scarce annotated data. However, existing frameworks, such as the Mean Teacher (MT), often suffer from low-quality pseudo-labels and limited robustness due to structural homogeneity and noise amplification in complex medical scenarios. To address these issues, this study presents a novel Dual-Student Mean Teacher Network (DSMT-Net), which enhances performance through a collaborative complementary architecture and pixel-level pseudo-label optimization. First, DSMT-Net combines U-Net and Mamba-UNet as dual students, utilizing the former's local boundary accuracy and the latter's global dependency modeling via a state-space model. Second, a pixel-level pseudo-label enhancement mechanism is introduced, combining pixel-level similarity analysis, adaptive confidence threshold setting, and iterative propagation to improve pseudo-label quality while maintaining structural consistency. Third, a self-supervised contrastive loss is adopted to enforce feature consistency between the dual students, alleviating noise propagation and improving the efficiency of unsupervised learning. Comprehensive evaluations on the ACDC and LA datasets confirm the effectiveness of DSMT-Net, highlighting its substantial capability to lower annotation requirements in medical image segmentation tasks. This provides a robust and scalable framework for semi-supervised learning in medical image segmentation, advancing clinical diagnostic efficiency and accuracy. Our code is available at https://github.com/sunwenlong1/DSMT.git.

Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation

Semi-supervised medical image segmentation via hard positives oriented contrastive learning