Multimodal ultrasound imaging, combining B-mode ultrasound, shear wave velocity, and shear wave time, plays a crucial role in breast tumor diagnosis by providing detailed insights into tumor characteristics. However, challenges such as inter-modal feature misalignment and attention drift complicate accurate segmentation due to variations in how each modality captures tissue properties. To address issues, we propose the MusoMamba framework and the MUB2025 dataset. MusoMamba enhances segmentation accuracy through bidirectional alignment and region-specific feature enhancement, utilizing the Modality-Collaborative Learning and Modality-Guided Enhancement modules. The MUB2025 dataset, comprising paired images across the three modalities from 506 cases, supports detailed analysis and model training. MusoMamba achieves strong results on MUB2025, with a 72.67
To address the issues of poor handling of ground object change boundary details in complex scenes and insufficient synergy caused by the decoupling of semantic and change subtasks in existing semantic change detection methods for remote sensing images, this paper proposes an improved algorithm based on contrastive learning. A Cross-Layer Attention Fusion FPN Decoder is designed. Through a multi-stage feature fusion strategy, it integrates low-level spatial details and high-level contextual information to generate more discriminative feature representations. Additionally, a Pixel-Level Change Modeling with Classification Features method is proposed, which shifts change modeling from the decision layer to the feature fusion stage. A dynamic change-aware loss is introduced to establish a collaborative training mechanism between the semantic segmentation and change detection tasks, enhancing the model’s sensitivity to change regions. In contrastive learning, a boundary hard sample mining strategy is proposed to prioritize the optimization of category boundary pixels, strengthening the model’s ability to recognize complex boundaries. The method is validated on two public datasets. Experimental results show that, compared with existing methods, the proposed method achieves significant improvements on multiple evaluation metrics for the tasks of change detection and semantic segmentation.
Although vision foundation models like SAM2 excel at natural image segmentation, their direct application to medical image segmentation faces severe domain gap challenges due to unique imaging principles, complex noise characteristics, and specialized semantic requirements. We propose AMoE-SAM2UNet, a cross-domain medical image segmentation framework that integrates an Adaptive Mixture of Experts Decoder (AMoE-Decoder) with the SAM2 encoder. The AMoE-Decoder comprises three specialized expert modules: the Multi-Scale Adaptive Expert (MSAE) captures lesion scale variations through parallel dilated convolutions and adaptive fusion operations; the Boundary Refinement Expert (BRE) sharpens blurred boundaries by enhancing high-frequency information via wavelet transform; and the Foreground Enhancement Expert (FEE) leverages multi-branch convolutions and spatial-channel attention to enhance foreground-background discrimination, addressing low contrast and complex background. Moreover, a lightweight gating network dynamically combines expert outputs based on input characteristics. Extensive experiments on five diverse medical imaging modalities—dermoscopy (ISIC 2017), ultrasound (TN3K), endoscopy (ClinicDB), CT (COVID), and histology (GLAS)—demonstrate that AMoE-SAM2UNet achieves consistent Dice coefficient improvements ranging from 1.8
Fluorescein Fundus Angiography (FFA), although widely used to visualize the retinal vasculature to monitor Diabetic retinopathy (DR), typically requires the intravenous injection of sodium fluorescein, a fluorescent contrast agent, making it an invasive procedure. In contrast, Color Fundus (CF) images are much more accessible and cheaper to collect. Generation of FFA based on CF thus become an effective approach to address such problem. However, previous works and existing image generation methods often struggle to preserve fine-grained details and anatomical integrity in terms of FFA generation. To address this limitation, we propose the Guidance-Controllable Mamba Diffusion Model (OTMamba) capable of generating high-fidelity FFA images directly from CF photographs. In contrast to existing GAN-based and diffusion-based medical image generation approaches that suffer from high computational complexity, limited scalability, and poor transferability, our proposed method demonstrates enhanced performance and clinical relevance in ophthalmology image translation. Experimental results confirm the model’s superiority in synthesizing diagnostically valuable FFA images, providing a promising non-invasive alternative for diabetic retinopathy screening and management.
