The Medical Segment Anything Model (MedSAM) has demonstrated strong performance in medical image segmentation, attracting increasing attention in the medical imaging domain. However, as with many prompt-based segmentation models, its performance is highly sensitive to the type and location of input prompts. This sensitivity often leads to suboptimal segmentation outcomes and necessitates labor-intensive manual prompt tuning, which hampers both efficiency and robustness. To address this challenge, this paper proposes MedSAM-U, an uncertainty-guided framework designed to automatically refine prompt inputs and enhance segmentation reliability. Specifically, a Multi-Prompt Adapter is integrated into MedSAM, resulting in MPA-MedSAM, which enables the model to effectively accommodate diverse multi-prompt inputs. An uncertainty estimation module is then introduced to evaluate the reliability of the prompts and their initial segmentation results. Based on this, a novel uncertainty-guided prompt adaptation strategy is applied to automatically generate refined prompts and more accurate segmentation outputs. The proposed MedSAM-U framework is evaluated across multiple medical imaging modalities. Experimental results on five diverse datasets demonstrate that MedSAM-U achieves consistent performance improvements ranging from 1.7% to 20.5% over the baseline MedSAM, confirming its effectiveness and practicality for robust and efficient medical image segmentation.

Medical image diagnosis relies heavily on subtle, fine-grained details, making it crucial to integrate global imaging representations with localized pathological information. Existing vision-language pretraining models have shown promise in utilizing free-text radiology reports for deeper semantic insights; however, they face significant challenges in precisely capturing and aligning region-specific, fine-grained information, which often results in suboptimal feature extraction for localized pathologies and reduced model interpretability. In this paper, we propose a unified multimodal multi-granularity pre-training framework (UMMPF) that explicitly models the local anatomical regions in chest X-ray images through a Region Querying Module (RQM), thereby establishing stronger correspondences between image sub-regions and textual descriptions. We further incorporate cross-modal bidirectional attention and unified multiple training objectives, facilitating the interaction of both global and local features across modalities. Our method enhances interpretability by explicitly mapping pathological findings to anatomical regions. Experimental results on benchmark datasets (RSNA, SIIM, and ChestX-ray14) demonstrate a significant improvement in diagnostic accuracy.

Diffusion models have shown remarkable promise for image restoration by leveraging powerful priors. Prominent methods typically frame the restoration problem within a Bayesian inference framework, which iteratively combines a denoising step with a likelihood guidance step. However, the interactions between these two components in the generation process remain underexplored. In this paper, we analyze the underlying gradient dynamics of these components and identify significant instabilities. Specifically, we demonstrate conflicts between the prior and likelihood gradient directions, alongside temporal fluctuations in the likelihood gradient itself. We show that these instabilities disrupt the generative process and compromise restoration performance. To address these issues, we propose Stabilized Progressive Gradient Diffusion (SPGD), a novel gradient management technique. SPGD integrates two synergistic components: (1) a progressive likelihood warm-up strategy to mitigate gradient conflicts; and (2) adaptive directional momentum (ADM) smoothing to reduce fluctuations in the likelihood gradient. Extensive experiments across diverse restoration tasks demonstrate that SPGD significantly enhances generation stability, leading to state-of-the-art performance in quantitative metrics and visually superior results. Code is available at https://github.com/74587887/SPGD.

Computed tomography (CT) stands as a ubiquitous medical diagnostic tool. Nonetheless, the radiation-related concerns associated with CT scans have raised public apprehensions. Mitigating radiation dosage in CT imaging poses an inherent challenge as it inevitably compromises the fidelity of CT reconstructions, impacting diagnostic accuracy. While previous deep learning techniques have exhibited promise in enhancing CT reconstruction quality, they remain hindered by the reliance on paired data, which is arduous to procure. In this study, we present a novel approach named Variational Score Solver (VSS) for sparse-view reconstruction without paired data. Our approach entails the acquisition of a probability distribution from densely sampled CT reconstructions, employing a latent diffusion model. High-quality reconstruction outcomes are achieved through an iterative process, wherein the diffusion model serves as the prior term, subsequently integrated with the data consistency term. Notably, rather than directly employing the prior diffusion model, we distill prior knowledge by finding the fixed point of the diffusion model. This framework empowers us to exercise precise control over the process. Moreover, we depart from modeling the reconstruction outcomes as deterministic values, opting instead for a distribution-based approach. This enables us to achieve more accurate reconstructions utilizing a trainable model. Our approach introduces a fresh perspective to the realm of zero-shot CT reconstruction, circumventing the constraints of supervised learning. Extensive qualitative and quantitative experiments unequivocally demonstrate that VSS surpasses other contemporary unsupervised and achieves comparable results compared to the most advanced supervised methods in sparse-view reconstruction tasks. Codes are available in https://github.com/fpsandnoob/vss.

In recent years, score-based diffusion models have emerged as effective tools for estimating score functions from empirical data distributions, particularly in integrating implicit priors with inverse problems like CT reconstruction. However, score-based diffusion models are rarely explored in challenging tasks such as metal artifact reduction (MAR). In this paper, we introduce a Bi-Constraints Diffusion Model for Metal Artifact Reduction (BCDMAR), an innovative approach that enhances iterative reconstruction with a conditional diffusion model for MAR. This method employs a metal artifact degradation operator in place of the traditional metal-excluded projection operator in the data-fidelity term, thereby preserving structure details around metal regions. However, score-based diffusion models tend to be susceptible to grayscale shifts and unreliable structures, making it challenging to reach an optimal solution. To address this, we utilize a pre-corrected image as a prior constraint, guiding the generation of the score-based diffusion model. By iteratively applying the score-based diffusion model and the data-fidelity step in each sampling iteration, BCDMAR effectively maintains reliable tissue representation around metal regions and produces highly consistent structures in non-metal regions. Through extensive experiments focused on metal artifact reduction tasks, BCDMAR demonstrates superior performance over other state-of-the-art unsupervised and supervised methods, both quantitatively and qualitatively.

Diffusion models have demonstrated remarkable efficacy in generating high-quality samples. Existing diffusion-based image restoration algorithms exploit pre-trained diffusion models to leverage data priors, yet they still preserve elements inherited from the unconditional generation paradigm. These strategies initiate the denoising process with pure white noise and incorporate random noise at each generative step, leading to over-smoothed results. In this paper, we present a refined paradigm for diffusion-based image restoration. Specifically, we opt for a sample consistent with the measurement identity at each generative step, exploiting the sampling selection as an avenue for output stability and enhancement. The number of candidate samples used for selection is adaptively determined based on the signal-to-noise ratio of the timestep. Additionally, we start the restoration process with an initialization combined with the measurement signal, providing supplementary information to better align the generative process. Extensive experimental results and analyses validate that our proposed method significantly enhances image restoration performance while consuming negligible additional computational resources.

Deep convolutional networks have obtained remarkable achievements on various visual tasks due to their strong ability to learn a variety of features. A well-trained deep convolutional network can be compressed to 20%–40% of its original size by removing filters that make little contribution, as many overlapping features are generated by redundant filters. Model compression can reduce the number of unnecessary filters but does not take advantage of redundant filters since the training phase is not affected. Modern networks with residual, dense connections and inception blocks are considered to be able to mitigate the overlap in convolutional filters, but do not necessarily overcome the issue. To do so, we propose a new training strategy, weight asynchronous update, which helps to significantly increase the diversity of filters and enhance the representation ability of the network. The proposed method can be widely applied to different convolutional networks without changing the network topology. Our experiments show that the stochastic subset of filters updated in different iterations can significantly reduce filter overlap in convolutional networks. Extensive experiments show that our method yields noteworthy improvements in neural network performance.