Personalized Federated Learning (pFL) effectively addresses the challenge of statistical heterogeneity in traditional Federated Learning (FL), with feature alignment methods (e.g., FedProto) standing out due to their communication efficiency and model-agnostic design, making them practically viable in real-world non-IID scenarios. These methods directly align class-level features across clients without requiring model parameter transmission. However, they represent each class as a holistic prototype, which limits the diversity and expressiveness of shared features. This restriction hampers the model's ability to generalize across clients and impedes personalized adaptation, as clients lack sufficient semantic components to reconstruct discriminative features tailored to their local data distributions. To overcome these limitations, we propose Federated Visual Primitive Learning (FedVPL), a novel framework comprising two key components: (1) Visual Primitive Space Sharing, which decomposes class-level features into semantically meaningful and reusable visual primitives, enabling cross-client and cross-class sharing to enrich feature diversity and decouple communication cost from the number of classes, significantly improving efficiency; and (2) Text-Guided Semantic Alignment, a parameter-free personalization mechanism that leverages external language priors to align shared primitives with client-specific semantics, without requiring additional communication overhead. Extensive experiments across diverse non-IID benchmarks demonstrate that FedVPL substantially outperforms state-of-the-art baselines, achieving up to a 5.62% improvement in accuracy, reducing communication overhead by at least 12.5x, and effectively addressing generalization and personalization challenges in heterogeneous federated environments.

Metal artifacts in computed tomography (CT) imaging significantly hinder diagnostic accuracy and clinical decision-making. While deep learning-based metal artifact reduction (MAR) methods have demonstrated promising progress, their clinical application is still constrained by three major challenges: 1) balancing metal artifact reduction with the preservation of critical anatomical structures, 2) effectively capturing the clinical priors of metal artifacts, and 3) dynamically adapting to polychromatic spectral variations. To address these limitations, in this paper, we propose a Self-supervised MAR method for computed Tomography (SMART) that leverages range-null space decomposition (RND) to model metal and tissue LACs separately, and employs implicit neural representation (INR) to learn their respective clinical characteristics without explicit supervision. Specifically, RND decouples metal and tissue LACs into a residual range component for metal LAC modeling, which captures metal artifacts, thus facilitating metal artifact reduction, and a null component for tissue LAC modeling, which focuses on preserving tissue details. To deal with the lack of paired data in clinical settings, we utilize INR to learn the clinical characteristics of these components in a self-supervised manner. Furthermore, SMART incorporates polychromatic spectra into the implicit representation, allowing dynamic adaptation to spectral variations across different imaging conditions. Extensive experiments on one synthetic and two clinical datasets demonstrate the strong potential of SMART in real-world scenarios. By flexibly adapting to spectral variations, it achieves superior generalizability to out-of-distribution clinical data.

Clinical practice reveals that patients frequently suffer from multiple co-occurring diseases, making multi-label classification (MLC) essential for accurate diagnosis. However, current MLC methods face two major challenges: (1) Disease-specific feature entanglement arising from the complex interdisease correlations among comorbidities; and (2) Untrustworthy results due to single-point estimates that lack confidence measurement. In this paper, we attempt to address these challenges at both the model and optimization levels. Specifically, at the model level, we introduce an improved transformer architecture with multi-CLS tokens for feature disentanglement. This architecture effectively captures the relationships among different diseases, while each CLS token integrates class-wise features, further refined by a multimodal method using a vision language model (VLM). At the optimization level, we propose a novel trustworthy MLC loss that aggregates positive/negative evidence for each class, modeling a multi-Beta distribution based on the Theory of Evidence, to generate reliable predictions with uncertainty estimations. Extensive experiments are conducted on publicly available clinical datasets, and the results demonstrate the effectiveness of our proposed method11The code is available at: https://github.com/CYYukio/Trustworthy-Disentangled-Framework..

Mobile and Web-of-Things (WoT) devices at the network edge generate vast amounts of data for machine learning applications, yet privacy concerns hinder centralized model training. Federated Learning (FL) allows clients (devices) to collaboratively train a shared model coordinated by a central server without transferring private data. However, inherent statistical heterogeneity among clients presents challenges, often leading to a dilemma between clients' need for personalized local models and the server's goal of building a generalized global model. Existing FL methods typically prioritize either global generalization or local personalization, resulting in a trade-off between these objectives and limiting the full potential of diverse client data. To address this challenge, we propose a novel framework that enhances both global generalization and local personalization by Rethinking Information Representation in the Federated learning process (FedRIR). Specifically, we introduce Masked Client-Specific Learning (MCSL), which isolates and extracts fine-grained client-specific features tailored to each client's unique data characteristics, thereby enhancing personalization. Meanwhile, the Information Distillation Module (IDM) refines global shared features by filtering out redundant client-specific information, resulting in a purer and more robust global representation that enhances generalization. By integrating refined global features with isolated client-specific features, we construct enriched representations that effectively capture both global patterns and local nuances, thereby improving the performance of downstream tasks on the client. Extensive experiments on diverse datasets demonstrate that FedRIR significantly outperforms state-of-the-art FL methods, achieving up to a 3.93% improvement in accuracy while ensuring robustness and stability in heterogeneous environments. The code is publicly available at https://github.com/Deep-Imaging-Group/FedRIR.

Lowering the radiation dose in computed tomography (CT) can greatly reduce the potential risk to public health. However, the reconstructed images from dose-reduced CT or low-dose CT (LDCT) suffer from severe noise which compromises the subsequent diagnosis and analysis. Recently, convolutional neural networks have achieved promising results in removing noise from LDCT images. The network architectures that are used are either handcrafted or built on top of conventional networks such as ResNet and U-Net. Recent advances in neural network architecture search (NAS) have shown that the network architecture has a dramatic effect on the model performance. This indicates that current network architectures for LDCT may be suboptimal. Therefore, in this paper, we make the first attempt to apply NAS to LDCT and propose a multi-scale and multi-level memory-efficient NAS for LDCT denoising, termed M3NAS. On the one hand, the proposed M3NAS fuses features extracted by different scale cells to capture multi-scale image structural details. On the other hand, the proposed M3NAS can search a hybrid cell- and network-level structure for better performance. In addition, M3NAS can effectively reduce the number of model parameters and increase the speed of inference. Extensive experimental results on two different datasets demonstrate that the proposed M3NAS can achieve better performance and fewer parameters than several state-of-the-art methods. In addition, we also validate the effectiveness of the multi-scale and multi-level architecture for LDCT denoising, and present further analysis for different configurations of super-net.

Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging procedure is inevitably contaminated by heavy speckle noise, which impairs both visual quality and diagnosis of various ocular diseases. Although deep learning has been applied for image denoising and achieved promising results, the lack of well-registered clean and noisy image pairs makes it impractical for supervised learning-based approaches to achieve satisfactory OCT image denoising results. In this paper, we propose an unsupervised OCT image speckle reduction algorithm that does not rely on well-registered image pairs. Specifically, by employing the ideas of disentangled representation and generative adversarial network, the proposed method first disentangles the noisy image into content and noise spaces by corresponding encoders. Then, the generator is used to predict the denoised OCT image with the extracted content features. In addition, the noise patches cropped from the noisy image are utilized to facilitate more accurate disentanglement. Extensive experiments have been conducted, and the results suggest that our proposed method is superior to the classic methods and demonstrates competitive performance to several recently proposed learning-based approaches in both quantitative and qualitative aspects. Code is available at: https://github.com/tsmotlp/DRGAN-OCT.

The current mainstream computed tomography (CT) reconstruction methods based on deep learning usually need to fix the scanning geometry and dose level, which significantly aggravates the training costs and requires more training data for real clinical applications. In this paper, we propose a parameter-dependent framework (PDF) that trains a reconstruction network with data originating from multiple alternative geometries and dose levels simultaneously. In the proposed PDF, the geometry and dose level are parameterized and fed into two multilayer perceptrons (MLPs). The outputs of the MLPs are used to modulate the feature maps of the CT reconstruction network, which condition the network outputs on different geometries and dose levels. The experiments show that our proposed method can obtain competitive performance compared to the original network trained with either specific or mixed geometry and dose level, which can efficiently save extra training costs for multiple geometries and dose levels.

Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstruction network that unrolls the iterative scheme and performs in both image and manifold spaces. Because patch manifolds of medical images have low-dimensional structures, we can build graphs from the manifolds. Then, we simultaneously leverage the spatial convolution to extract the local pixel-level features from the images and incorporate the graph convolution to analyze the nonlocal topological features in manifold space. The experiments show that our proposed method outperforms both the quantitative and qualitative aspects of state-of-the-art methods. In addition, aided by a projection loss component, our proposed method also demonstrates superior performance for semi-supervised learning. The network can remove most noise while maintaining the details of only 10% (40 slices) of the training data labeled.