In recent years, federated learning (FL) has received widespread attention for its ability to enable collaborative training across multiple clients while protecting user privacy, especially demonstrating significant value in scenarios such as medical data analysis, where strict privacy protection is required. However, most existing FL frameworks mainly focus on data heterogeneity without fully addressing the challenge of heterogeneous model aggregation among clients. To address this problem, this article proposes a novel FL framework called FedMKD. This framework introduces proxy models as a medium for knowledge sharing between clients, ensuring efficient and secure interactions while effectively utilizing the knowledge in each client's data. In order to improve the efficiency of asymmetric knowledge transfer between proxy models and private models, a hybrid feature-guided multilayer fusion knowledge distillation (MKD) learning method is proposed, which eliminates the dependence on public data. Extensive experiments were conducted using a combination of multiple heterogeneous models under diverse data distributions. The results demonstrate that FedMKD efficiently aggregates model knowledge.

Federated learning (FL) has been widely used in medical image processing to protect data privacy, but it has issues with data heterogeneity. Personalized federated learning have emerged to tackle these issues but often focuses too much on personalized models at the expense of global models. To address these problem, we propose a personalized federated learning method CoGAP, using collaborative optimization to enhance both personalized and global model performance. On the client side, it employs adaptive weight aggregation to initialize personalized models and uses a training strategy based on L2regularization. On the server side, it implements a gradient accumulation-based aggregation method. These modules facilitate a collaborative optimization process where the global model guides the personalized models, and the personalized models provide positive feedback to the global model, ultimately leading to mutual improvement for both. We conducted extensive comparative experiments on the OCT2017 dataset, as well as the BloodMnist and PathMnist (subsets from MedMnist), to evaluate both global and personalized models. Results show that CoGAP own a unique strength which not only outperforms other personalized federated learning methods in personalization capability while also remarkably achieving satisfying global generalization. Code is available at https://github.com/lcyCQUPT/CoGAP.

Federated Learning (FL) is an advanced technology that tackles the challenge of blocked data arising from privacy concerns, enabling the training of deep learning models without the need for data sharing. However, FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a novel hybrid-supervised federated learning method (FedSLAG) that explores various types of annotations in medical imaging. To focus more on weakly-supervised and unsupervised scenarios within hybrid-supervised learning, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations (points and scribbles). The feature extraction module combines the features of multiple weakly-supervised clients and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models. Experiments on breast tumor and skin lesion segmentation tasks demonstrate significant efficiency gains as well as highly competitive segmentation accuracy in many hybrid-supervised situations. Our codes are available at: https://github.com/TrivenDev/FedSLAG.

Heterogeneous federated learning enables collaborative training across clients under dual heterogeneity of models and data, posing challenges for effective knowledge transfer. Federated mutual learning employs proxy models to bridge cross-model knowledge exchange; however, existing methods remain limited to direct alignment between the outputs of private and proxy models, ignoring the deep discrepancies in representation and decision spaces between them. Such cognitive biases cause knowledge to be transferred only at shallow levels and trigger performance bottlenecks. To address this, this paper proposes FedKWAZ to identify and exploit Knowledge Weak-Aware Zones (KWAZ)—spatial zones of deep knowledge misalignment between private and proxy models, further refined into Semantic Weak-Aware Zones and Decision Weak-Aware Zones, which characterize cognitive misalignments in representation and decision spaces as focal targets for enhanced bidirectional distillation. FedKWAZ designs a Hierarchical Adaptive Patch Mixing (HAPM) mechanism to generate multiple mixed samples and employs a Knowledge Discrepancy Perceptron (KDP) to select the samples exhibiting the largest representation and decision discrepancies, thereby mining critical KWAZ. These modules are integrated into a two-stage mutual learning framework, achieving global class-level representation-decision consistency alignment and local KWAZ-guided refinement, structurally bridging cognitive biases across heterogeneous mutual learning models. Experimental results on multiple datasets and model configurations demonstrate the superior performance of FedKWAZ.

Existing studies of multi-modality medical image segmentation tend to aggregate all modalities without discrimination and employ multiple symmetric encoders or decoders for feature extraction and fusion. They often overlook the different contributions to visual representation and intelligent decisions among multi-modality images. Motivated by this discovery, this paper proposes an asymmetric adaptive heterogeneous network for multi-modality image feature extraction with modality discrimination and adaptive fusion. For feature extraction, it uses a heterogeneous two-stream asymmetric feature-bridging network to extract complementary features from auxiliary multi-modality and leading single-modality images, respectively. For feature adaptive fusion, the proposed Transformer-CNN Feature Alignment and Fusion (T-CFAF) module enhances the leading single-modality information, and the Cross-Modality Heterogeneous Graph Fusion (CMHGF) module further fuses multi-modality features at a high-level semantic layer adaptively. Comparative evaluation with ten segmentation models on six datasets demonstrates significant efficiency gains as well as highly competitive segmentation accuracy. (Our code is publicly available at https://github.com/joker-527/AAHN).

The accurate segmentation of brain tumor is significant in clinical practice. Convolutional Neural Network (CNN)-based methods have made great progress in brain tumor segmentation due to powerful local modeling ability. However, brain tumors are frequently pattern-agnostic, i.e. variable in shape, size and location, which can not be effectively matched by traditional CNN-based methods with local and regular receptive fields. To address the above issues, we propose a shape-scale co-awareness network (S2CA-Net) for brain tumor segmentation, which can efficiently learn shape-aware and scale-aware features simultaneously to enhance pattern-agnostic representations. Primarily, three key components are proposed to accomplish the co-awareness of shape and scale. The Local-Global Scale Mixer (LGSM) decouples the extraction of local and global context by adopting the CNN-Former parallel structure, which contributes to obtaining finer hierarchical features. The Multi-level Context Aggregator (MCA) enriches the scale diversity of input patches by modeling global features across multiple receptive fields. The Multi-Scale Attentive Deformable Convolution (MS-ADC) learns the target deformation based on the multiscale inputs, which motivates the network to enforce feature constraints both in terms of scale and shape for optimal feature matching. Overall, LGSM and MCA focus on enhancing the scale-awareness of the network to cope with the size and location variations, while MS-ADC focuses on capturing deformation information for optimal shape matching. Finally, their effective integration prompts the network to perceive variations in shape and scale simultaneously, which can robustly tackle the variations in patterns of brain tumors. The experimental results on BraTS 2019, BraTS 2020, MSD BTS Task and BraTS2023-MEN show that S2CA-Net has superior overall performance in accuracy and efficiency compared to other state-of-the-art methods. Code: https://github.com/jiangyu945/S2CA-Net.

Due to reasons such as non-i.i.d. data distribution, the generation of a single global model through collaborative distributed clients often fails to meet the task requirements of individual clients. Moreover, data distribution shifts are likely to make clients vulnerable to attacks, requiring a trade-off between the performance of the global model and the personalization of client models. To address these issues, one approach is to configure personalized models for each client to meet their specific tasks, while also training local models to fulfill overall task requirements. In this paper, we propose a method called Personalized Federated Learning with Local Optimization Model (pFedLO) to achieve personalized federated learning. This method ensures adaptability of personalized models on each client while allowing the achievement of the server’s task objectives. Specifically, in each update, a certain amount of local models can be retained to generate local optimization models, which are used to improve the performance of the personalized models. Experimental results demonstrate that the pFedLO method performs well in personalized tasks, effectively utilizing client local models to enhance the performance of personalized models. Our code is available at https://github.com/LilRind/pFedLO.

Due to the continuous improvement of traffic analysis technology, traditional covert channels have become insecure and vulnerable to human sabotage. Blockchain technology has the characteristics of immutability and anonymity, making covert communication more unmonitored and robust. However, it also brings about low communication efficiency. In this article, we adopt the idea of transaction rounds and propose for the first time the construction of HMAC values order (HVO) scheme. Furthermore, we further propose a HMAC values and transaction matrices (HV-TM) scheme to improve communication efficiency. This article is the first to use the gas field to embed data to improve the embedding rate. Use the random numbers generated by the Mersenne Twister algorithm to disrupt the order of addresses to improve the concealment of reused addresses. Experiments have shown that the two schemes have higher communication efficiency and better embedding rate than existing schemes.

Accurate tumor segmentation in medical images plays an important role in clinical diagnosis and disease analysis. However, medical images usually have great complexity, such as low contrast of computed tomography (CT) or low spatial resolution of positron emission tomography (PET). In the actual radiotherapy plan, multimodal imaging technology, such as PET/CT, is often used. PET images provide basic metabolic information and CT images provide anatomical details. In this paper, we propose a 3D PET/CT tumor co-segmentation framework based on active contour model. First, a new edge stop function (ESF) based on PET image and CT image is defined, which combines the grayscale standard deviation information of the image and is more effective for blurry medical image edges. Second, we propose a background subtraction model to solve the problem of uneven grayscale level in medical images. Apart from that, the calculation format adopts the level set algorithm based on the additive operator splitting (AOS) format. The solution is unconditionally stable and eliminates the dependence on time step size. Experimental results on a dataset of 50 pairs of PET/CT images of non-small cell lung cancer patients show that the proposed method has a good performance for tumor segmentation.

This paper proposes a hybrid active contour model for tumor co-segmentation from PET/CT images. We incorporate the foreground of the CT image and the background of the PET image into a co-segmentation framework to establish a new energy functional. Different from existing methods, the proposed model incorporates an edge stopping function based on PET images. The proposed method has been evaluated on a data set of 50 pairs of PET/CT images of non-small cell lung cancer patients and compared with other single-mode segmentation methods and co-segmentation methods. Experimental results show that our model is more robust than other strategies in complex backgrounds.

Head and neck (H&N) cancers are among the most common cancers worldwide (5th leading cancer by incidence). Accurate segmentation of H&N tumors can improve the early diagnosis rate of cancers for timely treatment. H&N tumor segmentation challenge is the equidensity between the tumor and surrounding tissues, which shows low contrast in CT. In contrast, PET images can reflect the distinction between the lesion region and normal tissue through metabolic activity but show low spatial resolution. With the underlying assumption that each modality contains complementary information, we introduce a novel L2-Norm Scaled Transformer (NSTR) multi-modal segmentation method in PET-CT images. The proposed network comprises the Embedding block, L2-Norm Transformer blocks, 3D Deformable down-sampling blocks, and Feature fusion module, which can fully exploit the high sensitivity of PET images to tumors and the anatomical information of CT images. Our method proposes a powerful 3D fusion network that uses a U-shaped structure to exploit complementary features of different models at multiple scales to increase the cubical representations between different modalities. We conducted a comprehensive experimental analysis on the HECKTOR PET-CT dataset. The results indicated NSTR has powerful featured representation capability and surpasses the state-of-the-art H&N tumor segmentation methods in DSC, Jaccard, RVD, and HD95. (our code will be publicly available soon).

Recently, extraction of blood vessels has aroused widespread interests in medical image analysis. In this work, to accelerate convergence speed and enhance the representation for discriminative features, we introduce the residual block structure in the ResNet into the 3D U-Net, and construct a new 3D Residual U-Net architect to segment the hepatic and portal veins from abdominal CT volumes. In addition, we develop a weighted Dice loss function to cope with the challenges of pixel imbalance, vessel boundary segmentation and small vessels segmentation. Furthermore, based on the prediction results, the post-processing methods of 3D morphological closed operation and volume analysis are employed to smooth the surface of vessels and eliminate noise blocks, respectively. Compared with existing 3D DenseNet, FCN and 3D U-Net, the average Dice coefficients of our method in hepatic veins and portal veins segmentation are 71.7% and 76.5% respectively, which are superior to 55.3% and 53.9% of the 3D DenseNet, 60.2% and 75.6% of the FCN, and 66.4% and 73.9% of the 3D U-Net. Meanwhile, the cross validation results prove that our method is accurate and stable for liver vessel extraction.

This paper presents a factorization based active contour model for 2-phase texture segmentation. We utilize the local spectral histogram as the texture features, and then establish a novel energy function based on the theory of the matrix decomposition. Unlike the existing methods, we only choose the combination weights from object region and background region to handle the motion of curve. We compare the proposed method to the recently active contour methods and the experiments are performed on synthetic and the real-world images. The experimental results show that our model is more robust against the complex background than the other strategies.

Images with weak contrast, overlapped noise and texture of the object and background make many PDE based methods disabled. To address these problems, this paper presents a novel combined multi-scale variational framework level set segmentation model. Its level set formulation consists edge-based term, region-based term and shape constraint term. The edge-based term is constructed using a newly defined edge stopping function. The region-based term is derived from parameter-free Gaussian probability density function (pdf) and multiple Gaussian kernel are used to gray equalization. The shape constraint term is used to constrain contour evolution at different scales of image pyramid. For an intrinsic smoothing segmentation contours, the level set function is explicitly represented by B-spline basis functions. Finally, a convolution is used during the energy minimization. Experimental results on synthetic and real images validate the robustness and high accuracy boundaries detection for low contrast, noise and texture images.

This paper introduces an effective active contour model for texture segmentation. To improve the robustness against noise and illumination, a novel descriptor named local statistical variation degree (LSVD) is presented to express textural features, which uses corner point deletion and isolated region detection operations to eliminate image patches unrelated with object regions. And then the fused features combined LSVD with Gabor can be constructed to express image structure in many scene. During the texture segmentation stage, a factorization based fitting energy is proposed to measure the weights of representative features in the features computed from image regions. This fitting energy can be used to localize region boundary more accurately. Moreover, a boundary shrinking method is put forward to improve the reliability of representative features. By comparing our proposed method with the recent texture segmentation models on synthetic images and natural images, we demonstrate that the novel active contour model can obtain accurate segmentation results and is robust to noise, illumination and position of initial contour.