Segment Anything Model (SAM) has demonstrated state-of-the-art performance in most segmentation tasks. However, due to insufficient training in the medical domain, SAM’s ability to generalize to medical images is limited. Although preliminary efforts have fine-tuned SAM for the medical domain, the fine-tuned model still struggles with variability in medical tasks. Some recent studies have explored weakly supervised learning to mitigate SAM’s performance degradation in the medical domain. However, the effectiveness of weakly supervised learning is heavily dependent on the quality of weakly supervised information, with performance significantly dropping as the quality declines. Doctors’ attention is closely related to the target area during diagnosis. Integrating gaze information into SAM’s adaptation process for medical image segmentation enhances efficiency and significantly improves performance in medical tasks. In this paper, we first propose a Gaze-assisted medical segment Anything Model (GAM), which utilizes gaze information to enable the adaptation of SAM in medical images following doctor’s attention. It has two innovations: 1) Feature-level adaptation: Gaze Alignment (GA) learning makes the feature-level adaptation follow the doctor’s attention which mines the human guidance from gaze heatmaps and guides model to extract general features for downstream tasks. 2) Output-level adaptation: Gaze-Balance (GB) learning makes the output-level adaptation follow the doctor’s attention which utilizes gaze heatmaps to enhance the human-focused area and solve the problem of over/under segmentation from the output-level. Our promising results on 7 tasks with 12 targets have demonstrated the powerful adaptation ability of our GAM in the medical domain. Our GAM demonstrates significant potential for low-cost clinical assistance in medical diagnosis, enabling SAM to adapt to the medical image domain without disrupting clinical workflows. We have released the full source code on https://github.com/Ruiz1026/GAM.

Plug-and-play (PnP) methods offer an iterative strategy for solving image restoration (IR) problems in a zero-shot manner, using a learned discriminative denoiser as the implicit prior. More recently, a sampling-based variant of this approach, which utilizes a pre-trained generative diffusion model, has gained great popularity for solving IR problems through stochastic sampling. The IR results using PnP with a pre-trained diffusion model demonstrate distinct advantages compared to those using discriminative denoisers, i.e.,improved perceptual quality while sacrificing the data fidelity. The unsatisfactory results are due to the lack of integration of these strategies in the IR tasks. In this work, we propose a novel zero-shot IR scheme, dubbed Reconciling Diffusion Model in Dual (RDMD), which leverages only a single pre-trained diffusion model to construct two complementary regularizers. Specifically, the diffusion model in RDMD will iteratively perform deterministic denoising and stochastic sampling, aiming to achieve highfidelity image restoration with appealing perceptual quality. RDMD also allows users to customize the distortion-perception tradeoff with a single hyperparameter, enhancing the adaptability of the restoration process in different practical scenarios. Extensive experiments on several IR tasks demonstrate that our proposed method could achieve superior results compared to existing approaches on both the FFHQ and ImageNet datasets. Code is available at https://github.com/chongwang1024/rdmd.

Domain adaptation (DA) for cardiac ultrasound image segmentation is clinically significant and valuable. However, previous domain adaptation methods are prone to be affected by the incomplete pseudo label and low-quality target to source images. Human-centric domain adaptation has great advantages of human cognitive guidance to help model adapt to target domain and reduce reliance on labels. Doctor gaze trajectories contains a large amount of cross-domain human guidance. To leverage gaze information and human cognition for guiding domain adaptation, we propose gaze-assisted human-centric domain adaptation (GAHCDA), which reliably guides the domain adaptation of cardiac ultrasound images. GAHCDA includes following modules: (1) Gaze Augment Alignment (GAA): GAA enables the model to obtain human cognition general features to recognize segmentation target in different domain of cardiac ultrasound images like humans. (2) Gaze Balance Loss (GBL): GBL fused gaze heatmap with outputs which makes the segmentation result structurally closer to the target domain. The experimental results show that our proposed framework is able to segment cardiac ultrasound images more effectively in the target domain than GAN-based methods and other self-train based methods and shown great potential in clinical application.

Deep unfolding networks (DUN) have emerged as a pop-ular iterative framework for accelerated magnetic reso-nance imaging (MRI) reconstruction. However, conventional DUN aims to reconstruct all the missing information within the entire null space in each iteration. Thus it could be challenging when dealing with highly ill-posed degradation, often resulting in subpar reconstruction. In this work, we propose a Progressive Divide-And-Conquer (PDAC) strategy, aiming to break down the subsampling process in the actual severe degradation and thus per-form reconstruction sequentially. Starting from decomposing the original maximum-a-posteriori problem of accel-erated MRI, we present a rigorous derivation of the pro-posed PDAC framework, which could be further unfolded into an end-to-end trainable network. Each PDAC iter-ation specifically targets a distinct segment of moderate degradation, based on the decomposition. Furthermore, as part of the PDAC iteration, such decomposition is adaptively learned as an auxiliary task through a degradation predictor which provides an estimation of the decomposed sampling mask. Following this prediction, the sampling mask is further integrated via a severity conditioning mod-ule to ensure awareness of the degradation severity at each stage. Extensive experiments demonstrate that our pro-posed method achieves superior performance on the pub-licly available fastMRI and Stanford2D FSE datasets in both multi-coil and single-coil settings. Code is available at https://github.com/ChongWang1024/PDAC.

Shadow removal is a task aimed at erasing regional shadows present in images and reinstating visually pleasing natural scenes with consistent illumination. While recent deep learning techniques have demonstrated impressive performance in image shadow removal, their robustness against adversarial attacks remains largely unexplored. Furthermore, many existing attack frameworks typically allocate a uniform budget for perturbations across the entire input image, which may not be suitable for attacking shadow images. This is primarily due to the unique characteristic of spatially varying illumination within shadow images. In this paper, we propose a novel approach, called shadow-adaptive adversarial attack. Different from standard adversarial attacks, our attack budget is adjusted based on the pixel intensity in different regions of shadow images. Consequently, the optimized adversarial noise in the shadowed regions becomes visually less perceptible while permitting a greater tolerance for perturbations in non-shadow regions. The proposed shadow-adaptive attacks naturally align with the varying illumination distribution in shadow images, resulting in perturbations that are less conspicuous. Building on this, we conduct a comprehensive empirical evaluation of existing shadow removal methods, subjecting them to various levels of attack on publicly available datasets.

Two-photon microscopy is currently the preferred technique for in vivo vasculature imaging. The ability to generate high-resolution 3D images of blood vessels could improve our understanding of normal vascular physiology and disease pathogenesis. Automatic segmenting and mapping of these 3D blood vessel networks remain challenging due to vascular geometries' complexity and signal-to-noise ratio limitations. In this paper, we propose a novel segmentation network named HYbrid Swin Unet Transformers (HiSwin UT) to address this problem. The network explores the use of the Swin transformer to segment 3D volumetric vessels in vivo images acquired by two-photon microscopy. We propose to use a hybrid of hierarchical Swin transformers with 3D convolutional neural networks (3D CNNs) as both the encoder and the decoder. The Swin transformer guards guide the 3D CNN laborers at every stage in both the encoding and decoding paths to enlarge the receptive field of the network. Specifically, the 3D vasculature images are projected into a sequence of embedding and fed into the network. The encoders extract features at five different resolutions by using a combination of sifted windows self-attention layers and residual convolutional layers. Then these latent multi-scale features are connected to each decoder via skip connection to cater to the diverse range of vessels in terms of their sizes and shapes. We evaluate this HiSwin UT model on the public two-photon vasculature dataset, extensive experimental results demonstrate the superiority of our network compared with the state-of-the-art models. The source code of the HiSwin UT will be publicly available at https://github.com/UTINK/HiSwinUT.

Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's capacity in shadow removal via progressively refining the desired output with both degradation prior and diffusive generative prior, which by nature can serve as a new strong baseline for image restoration. Furthermore, ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary task of the diffusion generator, which leads to more accurate and robust shadow-free image generation. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to validate our method's effectiveness. Compared to the state-of-the-art methods, our model achieves a significant improvement in terms of PSNR, increasing from 31.69dB to 34. 73dB over SRD dataset.11https://github.com/GuoLanqing/ShadowDiffusion

Recent deep learning methods have achieved superior results in shadow removal. However, most of these supervised methods rely on training over a huge amount of shadow and shadow-free image pairs, which require laborious annotations and may end up with poor model generalization. Shadows, in fact, only form partial degradation in images, while their non-shadow regions provide rich structural information potentially for unsupervised learning. In this paper, we propose a novel diffusion-based solution for unsupervised shadow removal, which separately modeling the shadow, non-shadow, and their boundary regions. We employ a pretrained unconditional diffusion model fused with non-corrupted information to generate the natural shadow-free image. While the diffusion model can restore the clear structure in the boundary region by utilizing its adjacent non-corrupted contextual information, it fails to address the inner shadow area due to the isolation of the non-corrupted contexts. Thus we further propose a Shadow-Invariant Intrinsic Decomposition module to exploit the underlying reflectance in the shadow region to maintain structural consistency during the diffusive sampling. Extensive experiments on the publicly available shadow removal datasets show that the proposed method achieves a significant improvement compared to existing unsupervised methods, and even is comparable with some existing supervised methods.

Image restoration schemes based on the pre-trained deep models have received great attention due to their unique flexibility for solving various inverse problems. In particular, the Plug-and-Play (PnP) framework is a popular and powerful tool that can integrate an off-the-shelf deep denoiser for different image restoration tasks with known observation models. However, obtaining the observation model that exactly matches the actual one can be challenging in practice. Thus, the PnP schemes with conventional deep denoisers may fail to generate satisfying results in some real-world image restoration tasks. We argue that the robustness of the PnP framework is largely limited by using the off-the-shelf deep denoisers that are trained by deterministic optimization. To this end, we propose a novel deep reinforcement learning (DRL) based PnP framework, dubbed RePNP, by leveraging a light-weight DRL-based denoiser for robust image restoration tasks. Experimental results demonstrate that the proposed RePNP is robust to the observation model used in the PnP scheme deviating from the actual one. Thus, RePNP can generate more reliable restoration results for image deblurring and super resolution tasks. Compared with several state-of-the-art deep image restoration baselines, RePNP achieves better results subjective to model deviation with fewer model parameters.