Foundation models, such as the Segment Anything Model (SAM), have demonstrated impressive generalization across various image segmentation tasks. However, they encounter challenges when applied to medical imaging, primarily due to the lack of domain-specific expertise and the limited availability of annotated data. Existing methods for adapting SAM typically rely on expert-driven prompt design and extensive fine-tuning, which hinder their effectiveness in medical imaging, particularly for rare and complex anatomical structures. To overcome these challenges, we propose MASG-SAM, an innovative framework designed for efficient few-shot medical image segmentation. MASG-SAM integrates three key innovations: the Hierarchical Attention Enhancement (HAE), Boundary Feature Enhancement (BFE), and Dynamic Semantic Fusion (DSF) modules. The HAE module optimizes attention distribution across hierarchical feature maps, enhancing feature diversity and reducing feature drift, thereby improving segmentation of both global and local features in complex medical images. The BFE module introduces a boundary-sensitive mechanism that enhances edge detection, enabling precise segmentation of overlapping or difficult-to-delineate anatomical structures. Finally, the DSF module leverages Contrastive Language-Image Pretraining (CLIP) to inject domain-specific medical semantic knowledge. By adaptively refining feature fusion during training, DSF combines semantic guidance with spatial adjustments, progressively improving segmentation accuracy, particularly in data-scarce scenarios. Experiments conducted on four publicly available medical datasets show that MASG-SAM outperforms state-of-the-art methods, achieving high segmentation accuracy with minimal labeled data. Our framework significantly enhances the adaptability and accuracy of SAM in complex medical imaging tasks.

The Segment Anything Model (SAM) excels in general segmentation but encounters difficulties in medical imaging due to few-shot learning challenges, particularly with extremely limited annotated data. Existing approaches often suffer from insufficient feature extraction and inadequate loss function balancing, resulting in decreased accuracy and poor generalization. To address these issues, we propose BiASAM, which uniquely incorporates two bidirectional attention mechanisms into SAM for medical image segmentation. Firstly, BiASAM integrates a spatial-frequency attention module to improve feature extraction, enhancing the model's ability to capture both fine and coarse details. Secondly, we employ an attention-based gradient update mechanism that dynamically adjusts loss weights, boosting the model's learning efficiency and adaptability in data-scarce scenarios. Additionally, BiASAM utilizes the point and box fusion prompt to enhance segmentation precision at both global and local levels. Experiments across various medical datasets show BiASAM achieves performance comparable to fully supervised methods with just two labeled samples.

Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained model to unlabeled target domain data without access to source domain data, presenting a significant challenge for medical image segmentation. Most current approaches address this challenge through self-training, employing manually augmented target domain images and pseudo-labels to enforce consistency regularization. However, these approaches still encounter two primary issues. Firstly, manually augmented consistency self-training results in performance degradation due to the semantic mismatch between the target domain images and noisy pseudo-labels. Secondly, they fail to fully exploit the informative content present in the target domain, exhibiting inadequate adaptability, particularly in significant domain gaps. To address these, we introduce the Diffusion-driven Dual-flow SFDA (D2SFDA), the pioneering framework to integrate a diffusion model into SFDA for medical image segmentation. Our D2SFDA framework comprises two novel components: the Diffusion Perturbation Flow (DPF) and the Twin-Knowledge Investigation Flow (TKIF). DPF utilizes pseudo-labels to generate diverse and semantically consistent diffusion views, providing more realistic supervision, potentially enhancing model stability. Surprisingly, DPF using only diffusion images outperforms self-training using real images, as evidenced by the superior average Dice score on the BASE1 target domain of the RIGA+ dataset (90.31% vs. 85.64%). Additionally, TKIF rigorously analyzes the target domain with dual-focus consistency regularization on domain-invariant and target domain-specific knowledge, effectively reducing domain gaps, resulting in an improvement from 90.31% to 91.79%. Extensive experiments on two cross-domain datasets confirm that our D2SFDA surpasses state-of-the-art SFDA approaches in effectively addressing domain shift issues. The code is available at https://github.com/M4cheal/D2SFDA.

In recent years, federated learning has been rapidly developing as an emerging privacy computing method. Its unique distributed computing feature enables multiple participants to collaborate on modelling while ensuring that the data remains local and only the model parameters are passed on, thus effectively preserving privacy. This characteristic has led to the gradual introduction of Federated learning into real-world engineering applications, particularly in the financial sector. Banks, insurance companies, and other financial institutions are able to integrate financial data from multiple enterprises through federated learning to effectively perform a range of important financial tasks. However, financial data usually contains a large amount of personal information, and in addition to its privacy, unprocessed character data is difficult to be directly applied to models. Therefore, how to efficiently encode and convert these data into digital features has become an important problem to be solved in federated learning. The paper introduces a WOE(Weight Of Evidence) encoding method within a vertical federated learning framework, designed to maintain label confidentiality. This approach allows unlabelled participants to leverage label information for effective character feature WOE encoding, enhancing data utility while ensuring privacy. A classification task model for insurance anti-fraud is constructed and a series of evaluation metrics are used to demonstrate the effectiveness of the proposed method compared to other unsupervised coding methods. It also verifies that federated learning can integrate multi-party data and thus improve the classification ability of anti-fraud models.

Segmentation of the Optic Disc (OD) and Optic Cup (OC) is crucial for the early detection and treatment of glaucoma. Despite the strides made in deep neural networks, incorporating trained segmentation models for clinical application remains challenging due to domain shifts arising from disparities in fundus images across different healthcare institutions. To tackle this challenge, this study introduces an innovative unsupervised domain adaptation technique called Multi-scale Adaptive Adversarial Learning (MAAL), which consists of three key components. The Multi-scale Wasserstein Patch Discriminator (MWPD) module is designed to extract domain-specific features at multiple scales, enhancing domain classification performance and offering valuable guidance for the segmentation network. To further enhance model generalizability and explore domain-invariant features, we introduce the Adaptive Weighted Domain Constraint (AWDC) module. During training, this module dynamically assigns varying weights to different scales, allowing the model to adaptively focus on informative features. Furthermore, the Pixel-level Feature Enhancement (PFE) module enhances low-level features extracted at shallow network layers by incorporating refined high-level features. This integration ensures the preservation of domain-invariant information, effectively addressing domain variation and mitigating the loss of global features. Two publicly accessible fundus image databases are employed to demonstrate the effectiveness of our MAAL method in mitigating model degradation and improving segmentation performance. The achieved results outperform current state-of-the-art (SOTA) methods in both OD and OC segmentation.

Recently, end-to-end deep neural networks-based glaucoma diagnosis approaches have been gaining much attention. However, the feature extractor and classier in these approaches are trained together, which is known as coadaptation. Therefore, the feature distribution in them should adapt to particular decision boundaries. To learn generic data representations and improve the generalization ability of the model, this paper designs a three-stage framework for glaucoma diagnosis. In the first stage, preprocessing is utilized to extract the Region of Interesting around the Optic Disc to reduce the computational cost and nonobjective interference. In the second stage, Deep Sparse Autoencoder is designed to learn hybrid features between the deep features and the original features, which could improve the effectiveness of final high-level feature expression. Meanwhile, L1 regularization is introduced and applied on the hybrid features to obtain deep features with high complementarity under small sample problem. In the third stage, the obtained generic feature representations are fed into different classifiers, in which Support Vector Machine classifier achieves the best diagnosis performance. The proposed approach is evaluated on two publicly available databases. Extensive experimental results indicate that our approach outperforms the state-of-the-art approaches with the accuracy of 96.00%, 97.00% and Area Under Curve of 96.94%, 98.28% for REFUGE and Drishti-GS1 databases, respectively.

As one of the most effective feature learning methods, Nonnegative Matrix Factorization (NMF) has been widely used in many scientific fields, such as computer vision, data mining, and bioinformatics. However, NMF is an unsupervised method that cannot fully utilize the label information of data. Thus, its performance is limited in some recognition and classification problems. To remedy this shortcoming, this paper proposes a Semisupervised Discriminative NMF (SDNMF) method. First, we design a Soft-Labeled NMF (SLNMF) model by introducing a soft-label matrix-based regression term into the original NMF, so that the relationship between the soft-label matrix and low-dimensional features can be constructed to improve the discriminative ability of low-dimensional features. Second, to effectively estimate the soft-label matrix, a Label Propagation (LP) model is adopted to fully explore the spatial distribution relationship between the labeled and unlabeled samples. Third, an Adaptive Graph Learning (AGL) model is proposed to exploit the geometric relationship of samples well, which could enhance the performance of LP. Finally, the above three models (i.e., SLNMF, LP, and AGL) are integrated into a unified framework for effective feature learning, which can not only effectively explore the structural relationship matrix between data, but also predict the labels for unknown samples. Moreover, an iterative optimization algorithm is presented to solve our objective function. The convergence and computational complexity analysis of the proposed SDNMF method are also provided. Extensive experiments are conducted on several standard data sets. Compared with related methods, the experimental results verify that the proposed SDNMF method achieves better performance.

Glaucoma is an eye disease that leads to irreversible vision loss. Accurate Optic Disc (OD) and Optic Cup (OC) segmentation can effectively facilitate ophthalmologist in glaucoma diagnosis. Recently, a series of deep learning approaches attain promising performance in OD and OC segmentation but still face the challenge to precisely segment OC boundary with enhanced computational efficiency. To address this issue, we propose a novel network named Robust Multiscale Feature Extraction with Depthwise Separable Convolution (RMSDSC-Net), which can better solve the challenging tradeoff between segmentation performance and network cost. The proposed RMSDSC-Net is mainly composed of Multiscale Input (MSI), Depthwise Separable Convolution Unit (DSCU), Dilated Convolution Block (DCB), and External Residual Connection (ERC). First, the introduction of MSI can reduce the information loss due to the pooling layers used in the network for capturing rich feature representations. Next, to enhance segmentation performance and computational efficiency, this paper designs DSCU and DCB modules to avoid spatial information loss from minor details of the image and preserve more high-level semantic features. Finally, this paper develops ERC established between the encoding layers and decoding layers to minimize the feature degradation problem. Hence, a high segmentation performance can be achieved using a shallow network. To evaluate the performance of the proposed network, extensive experiments have been enforced on two publicly available databases, DRISHTI-GS and REFUGE. Our approach outperforms the state-of-the-art approaches with the Dice Coefficient of (0.978, 0.919) and (0.965, 0.910) for OD and OC segmentation on DRISHTI-GS and REFUGE databases, respectively. As a result, the proposed approach has a strong potential in analyzing fundus images for glaucoma diagnosis.

Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases. In this work, we propose a new deep learning model, namely Channel Attention Residual U-Net (CAR-UNet), to accurately segment retinal vascular and non-vascular pixels. In this model, we introduced a novel Modified Efficient Channel Attention (MECA) to enhance the discriminative ability of the network by considering the interdependence between feature maps. On the one hand, we apply MECA to the "skip connections" in the traditional U-shaped networks, instead of simply copying the feature maps of the contracting path to the corresponding expansive path. On the other hand, we propose a Channel Attention Double Residual Block (CADRB), which integrates MECA into a residual structure as a core structure to construct the proposed CAR-UNet. The results show that our proposed CAR-UNet has reached the state-of-the-art performance on three publicly available retinal vessel datasets: DRIVE, CHASE DB1 and STARE.

With the rapid development of computer network technology, we can acquire a large amount of multimedia data, and it becomes a very important task to analyze these data. Since graph construction or graph learning is a powerful tool for multimedia data analysis, many graph‐based subspace learning and clustering approaches have been proposed. Among the existing graph learning algorithms, the sample reconstruction‐based approaches have gone the mainstream. Nevertheless, these approaches not only ignore the local and global structure information but also are sensitive to noise. To address these limitations, this paper proposes a graph learning framework, termed Robust Graph Structure Learning (RGSL). Different from the existing graph learning approaches, our approach adopts the self‐expressiveness of samples to capture the global structure, meanwhile utilizing data locality to depict the local structure. Specially, in order to improve the robustness of our approach against noise, we introduce l2,1‐norm regularization criterion and nonnegative constraint into the graph construction process. Furthermore, an iterative updating optimization algorithm is designed to solve the objective function. A large number of subspace learning and clustering experiments are carried out to verify the effectiveness of the proposed approach.

Retinal vessel segmentation plays an imaportant role in the field of retinal image analysis because changes in retinal vascular structure can aid in the diagnosis of diseases such as hypertension and diabetes. In recent research, numerous successful segmentation methods for fundus images have been proposed. But for other retinal imaging modalities, more research is needed to explore vascular extraction. In this work, we propose an efficient method to segment blood vessels in Scanning Laser Ophthalmoscopy (SLO) retinal images. Inspired by U-Net, "feature map reuse" and residual learning, we propose a deep dense residual network structure called DRNet. In DRNet, feature maps of previous blocks are adaptively aggregated into subsequent layers as input, which not only facilitates spatial reconstruction, but also learns more efficiently due to more stable gradients. Furthermore, we introduce DropBlock to alleviate the overfitting problem of the network. We train and test this model on the recent SLO public dataset. The results show that our method achieves the state-of-the-art performance even without data augmentation.

Non-negative matrix factorization (NMF) has recently attracted much attention due to its good interpretation in perception science and widely applications in various fields. In this paper, a novel graph regularized NMF algorithm called NMF with locality constrained adaptive graph (NMF-LCAG) is proposed. Compared with other NMF based algorithms, the proposed NMF-LCAG algorithm has the following advantages: 1) Unlike the traditional NMF method which neglects the geometric information of original data, the proposed algorithm introduces a locality constrained graph to discover the latent manifold structure of the data and 2) Different from most graph regularized NMF algorithms in which the graphs are predefined and kept unchanged during the NMF procedure, two locality constraint terms are employed in our NMF-LCAG to adaptively optimize the graph. Thus, the weight matrix of graph and low dimensional features of data can be simultaneously learned by our algorithm, which makes NMF-LCAG more flexible than other approaches. Moreover, an iterative updating strategy is developed to optimize the objective function of our algorithm and the convergence analysis is also given. Extensive experiments are conducted on four face image databases and three UCI datasets to demonstrate the effectiveness of the proposed NMF-LCAG algorithm. Compared with some other related algorithms, the proposed NMF-LCAG algorithm can achieve at least 1% ~ 3% accuracy improvement in most cases.

At present, artificial visual diagnosis of fundus diseases has low manual reading efficiency and strong subjectivity, which easily causes false and missed detections. Automatic segmentation of retinal blood vessels in fundus images is very effective for early diagnosis of diseases such as the hypertension and diabetes. In this paper, we utilize the U-shaped structure to exploit the local features of the retinal vessels and perform retinal vessel segmentation in an end-to-end manner. Inspired by the recently DropBlock, we propose a new method called Structured Dropout U-Net (SD-Unet), which abandons the traditional dropout for convolutional layers, and applies the structured dropout to regularize U-Net. Compared to the state-of-the-art methods, we demonstrate the superior performance of the proposed approach.