Medical vision-language pre-training (VLP) offers significant potential for advancing medical image understanding by leveraging paired image-report data. However, existing methods are limited by False Negatives (FaNe) induced by semantically similar texts and insufficient fine-grained cross-modal alignment. To address these limitations, we propose FaNe, a semantic-enhanced VLP framework. To mitigate false negatives, we introduce a semantic-aware positive pair mining strategy based on text-text similarity with adaptive normalization. Furthermore, we design a text-conditioned sparse attention pooling module to enable fine-grained image-text alignment through localized visual representations guided by textual cues. To strengthen intra-modal discrimination, we develop a hard-negative aware contrastive loss that adaptively reweights semantically similar negatives. Extensive experiments on five downstream medical imaging benchmarks demonstrate that FaNe achieves state-of-the-art performance across image classification, object detection, and semantic segmentation, validating the effectiveness of our framework.

Confidence-based pseudo-label selection usually generates overly confident yet incorrect predictions, due to the early misleadingness of model and overfitting inaccurate pseudo-labels in the learning process, which heavily degrades the performance of semi-supervised contrastive learning. Moreover, segmentation and classification tasks are treated independently and the affinity fails to be fully explored. To address these issues, we propose a novel semi-supervised dual-threshold contrastive learning strategy for ultrasound image classification and segmentation, named Hermes. This strategy combines the strengths of contrastive learning with semi-supervised learning, where the pseudo-labels assist contrastive learning by providing additional guidance. Specifically, an inter-task attention and saliency module is also developed to facilitate information sharing between the segmentation and classification tasks. Furthermore, an inter-task consistency learning strategy is designed to align tumor features across both tasks, avoiding negative transfer for reducing features discrepancy. To solve the lack of publicly available ultrasound datasets, we have collected the SZ-TUS dataset, a thyroid ultrasound image dataset. Extensive experiments on two public ultrasound datasets and one private dataset demonstrate that Hermes consistently outperforms several state-of-the-art methods across various semi-supervised settings. The code is available at https://github.com/Aventador8/Hermes.

In order to obtain good medical image segmentation results, existing studies usually extract multi-scale features or design special attention to obtain global and local information of images. However, the above methods are very cumbersome or have a high computational burden. Theoretically, global and local contexts are used to locate objects and refine their contours respectively. Therefore, from a novel points prediction perspective, this work adopts a convolutional model and employs the deep supervision method to assist segmentation by predicting points of the lesion location and boundary. Specifically, we use two points prediction branches to generate location and boundary heatmaps, which implicitly locate lesions and refine contours. Furthermore, in order to train the multi-task model effectively, this work proposes a simple supervision signals design principle to guide deep supervision, so that the training of the auxiliary branch does not conflict with the main task branch. It is worth noting that our simple and end-to-end approach achieves the state-of-the-art results without the need for post-processing. Extensive experiments on three challenging medical image segmentation benchmarks demonstrate the superior performance.

Subspace learning and Support Vector Machine (SVM) are two critical techniques in pattern recognition, playing pivotal roles in feature extraction and classification. However, how to learn the optimal subspace such that the SVM classifier can perform the best is still a challenging problem due to the difficulty in optimization, computation, and algorithm convergence. To address these problems, this paper develops a novel method named Optimal Discriminant Support Vector Machine (ODSVM), which integrates support vector classification with discriminative subspace learning in a seamless framework. As a result, the most discriminative subspace and the corresponding optimal SVM are obtained simultaneously to pursue the best classification performance. The efficient optimization framework is designed for binary and multi-class ODSVM. Moreover, a fast sequential minimization optimization (SMO) algorithm with pruning is proposed to accelerate the computation in multi-class ODSVM. Unlike other related methods, ODSVM has a strong theoretical guarantee of global convergence, highlighting its superiority and stability. Numerical experiments are conducted on thirteen datasets and the results demonstrate that ODSVM outperforms existing methods with statistical significance.

Medical image segmentation is a challenging task especially when dealing with unlabeled data. It has been proven that the use of complementary information for co-training is effective for medical image segmentation. We find that high-confidence regions are easy to segment, but low-confidence regions are difficult to correctly segment, which results in poor segmentation performance. Therefore, accurate segmentation in low-confidence regions is the effective way to enhance the performance of medical image segmentation. To achieve this goal, we propose a novel co-training strategy named low-confidence heterogeneous pixel-prototype contrastive learning with uncertainty-guide cross supervision for semi-supervised medical image segmentation. Specifically, an uncertainty-guided cross supervision module is firstly designed to estimate the confidence score of predictions from multiple outputs and then perform cross supervision between high-confidence regions. Moreover, we design a low-confidence heterogeneous prototype contrastive learning, which builds the relationship between low-confidence pixel and heterogeneous prototype to mine discriminative information in low-confidence regions so as to improve the segmentation performance. Extensive experiments on medical image datasets demonstrate that our method outperforms state-of-the-art methods in segmentation results.

Hash learning aims to map multimedia data into Hamming space, in which the data point is represented by low-dimensional binary codes and the similarity relationships are preserved. Despite existing hash learning methods have been effectively used in data retrieval tasks for its merits of low memory cost and high computational efficiency, there still remain two major technical challenges. Firstly, due to the discrete constraints of hash codes, traditional hash methods typically use relaxation strategy to learn real-value features and then quantize them into binary codes through a sign function, resulting in significant quantization errors. Secondly, hash codes are usually low-dimensional, which would be inadequate to preserve either the information of each data point or the relationship between two. These two challenges would greatly limit the retrieval performance of learned hash codes. To solve these problems, we introduce a novel quantization method called scaling factor quantization to enhance hash learning. Unlike traditional hashing methods, we propose to map the data into two parts, i.e., hash codes and scaling factors, to learn the representative codes for the use of retrieval. Specifically, we design a multi-output branch network structure, i.e., Deep Scaling factor Quantization Network (DSQN) and an iterative training strategy for DSQN to learn the two parts of mapping. Comprehensive experiments conducted on three benchmark datasets demonstrate that the hash codes and scaling factors learned by DSQN significantly improve retrieval accuracy compared to existing hash learning methods.

High-dimensional small sample size data, which may lead to singularity in computation, are becoming increasingly common in the field of pattern recognition. Moreover, it is still an open problem how to extract the most suitable low-dimensional features for the support vector machine (SVM) and simultaneously avoid singularity so as to enhance the SVM's performance. To address these problems, this article designs a novel framework that integrates the discriminative feature extraction and sparse feature selection into the support vector framework to make full use of the classifiers' characteristics to find the optimal/maximal classification margin. As such, the extracted low-dimensional features from high-dimensional data are more suitable for SVM to obtain good performance. Thus, a novel algorithm, called the maximal margin SVM (MSVM), is proposed to achieve this goal. An alternatively iterative learning strategy is adopted in MSVM to learn the optimal discriminative sparse subspace and the corresponding support vectors. The mechanism and the essence of the designed MSVM are revealed. The computational complexity and convergence are also analyzed and validated. Experimental results on some well-known databases (including breastmnist, pneumoniamnist, colon-cancer, etc.) show the great potential of MSVM against classical discriminant analysis methods and SVM-related methods, and the codes can be available on https://www.scholat.com/laizhihui.

Support vector machine (SVM), as a supervised learning method, has different kinds of varieties with significant performance. In recent years, more research focused on nonparallel SVM, where twin SVM (TWSVM) is the typical one. In order to reduce the influence of outliers, more robust distance measurements are considered in these methods, but the discriminability of the models is neglected. In this article, we propose robust manifold twin bounded SVM (RMTBSVM), which considers both robustness and discriminability. Specifically, a novel norm, that is, capped$L_{1}$-norm, is used as the distance metric for robustness, and a robust manifold regularization is added to further improve the robustness and classification performance. In addition, we also use the kernel method to extend the proposed RMTBSVM for nonlinear classification. We introduce the optimization problems of the proposed model. Subsequently, effective algorithms for both linear and nonlinear cases are proposed and proved to be convergent. Moreover, the experiments are conducted to verify the effectiveness of our model. Compared with other methods under the SVM framework, the proposed RMTBSVM shows better classification accuracy and robustness.

Mammogram mass detection is a difficult task due to the mass character of the tiny area, fuzzy boundary, and occlusion. To address these problems, this paper proposes a novel detection network for mammogram mass detection. Firstly, we propose a novel feature fusion structure and Small Target Attention Module (STAM) to improve the model's ability to detect small masses. Secondly, Results-oriented Loss (ROL) is adopted to obtain better model performance. Finally, Incremental Positive Selection (IPS) is used to divide positive and negative anchors. The scarcity of breast mammogram images for training aggravates the difficulty of mass detection. Thus, we open our collected dataset, which contains 1456 mammogram images from 400 patients. Since the model includes a double feature fusion structure, the proposed network is named Dual Fusion Mass Detector (DFMD). Experiment results show that DFMD is robust to various variations on scale, blurry and occlusion.

Breast disease is one of the primary diseases endangering women's health. Accurate segmentation of breast lesions can help doctors diagnose breast diseases. However, the size and morphology of breast lesions are different, and the intensity of breast tissue is uneven. Thus, it is challenging to segment the lesion area accurately. In this paper, we propose Dual-scale Feature Fusion (DSFF) module and Edgeloss to segment breast lesions. The DSFF module aims to integrate two-scale features and design another effective skip connection scheme to reduce false positive regions. To solve the problem of unclear segmentation boundary, we design Edgeloss for additional supervision on the boundary region to obtain a finer segmentation boundary. The experiment results show that the proposed DL-UNet with the DSFF module and new Edgeloss performs best in several classic networks.

Recently, contrastive learning has shown great potential in medical image segmentation. Due to the lack of expert annotations, however, it is challenging to apply contrastive learning in semi-supervised scenes. To solve this problem, we propose a novel uncertainty-guided pixel contrastive learning method for semi-supervised medical image segmentation. Specifically, we construct an uncertainty map for each unlabeled image and then remove the uncertainty region in the uncertainty map to reduce the possibility of noise sampling. The uncertainty map is determined by a well-designed consistency learning mechanism, which generates comprehensive predictions for unlabeled data by encouraging consistent network outputs from two different decoders. In addition, we suggest that the effective global representations learned by an image encoder should be equivariant to different geometric transformations. To this end, we construct an equivariant contrastive loss to strengthen global representation learning ability of the encoder. Extensive experiments conducted on popular medical image benchmarks demonstrate that the proposed method achieves better segmentation performance than the state-of-the-art methods.

As the extended version of conventional Ridge Regression, L2,1-norm based ridge regression learning methods have been widely used in subspace learning since they are more robust than Frobenius norm based regression and meanwhile guarantee joint sparsity. However, conventional L2,1-norm regression methods encounter the small-class problem and meanwhile ignore the local geometric structures, which degrade their performances. To address these problems, we propose a novel regression method called Locality Preserving Robust Regression (LPRR). In addition to using the L2,1-norm for jointly sparse regression, we also utilize capped L2-norm in loss function to further enhance the robustness of the proposed algorithm. Moreover, to make use of local structure information, we also integrate the property of locality preservation into our model since it is of great importance in dimensionality reduction. The convergence analysis and computational complexity of the proposed iterative algorithm are presented. Experimental results on four datasets indicate that the proposed LPRR performs better than some famous subspace learning methods in classification tasks.

With the rapid development of the Internet of Things and cloud computing technologies, the Internet of Things technology based on comprehensive perception and interconnection and cloud computing based on virtualization, dynamic resources, and parallel computing have become the driving force for the innovation and development of informatization and intelligence. The cloud‐based Internet of Things mobile medical is an ecosystem of health information and medical information, with the medical Internet of Things at its core and highly mobile and highly shared information. Therefore, in the context of in‐depth research on mobile medical care, research on mobile postoperative thyroid monitoring and management systems based on the Internet of Things and cloud computing is a practical tool for promoting the development of mobile medical systems. Monitoring and Management. In this experiment, 48 cases of patients undergoing thyroid surgery were selected from a hospital. The experimental group was informed by the experiment that they need to be equipped with sensors. The control panel and GPS positioning module are used to obtain the exact position of the patient at the first time. The loading of the sensor is agreed by the patient and the patient’s family. Afterward, the control group will not be processed. It will conduct functional tests and software performance tests on the mobile medical monitoring and management system and analyze the satisfaction of medical staff with the mobile medical monitoring and management system. Experiments have proved that the cloud computing medical monitoring and management system is used to obtain the exact location of the patient in the first time, and the response time needs to be shorter. The response time of the system increases with the increase of the number of sensors (P < 0.05), which shows that the mobile medical monitoring and management system is essential for medical care and medical care. Obtaining the exact position of the patient for the first time is of great importance for the successful rescue of the patient.

Microarray techniques have been used to delineate cancer groups or to identify candidate genes for cancer prognosis. As such problems can be viewed as classification ones, various classification methods have been applied to analyze or interpret gene expression data. In this paper, we propose a novel method based on robust principal component analysis (RPCA) to classify tumor samples of gene expression data. Firstly, RPCA is utilized to highlight the characteristic genes associated with a special biological process. Then, RPCA and RPCA+LDA (robust principal component analysis and linear discriminant analysis) are used to identify the features. Finally, support vector machine (SVM) is applied to classify the tumor samples of gene expression data based on the identified features. Experiments on seven data sets demonstrate that our methods are effective and feasible for tumor classification.