Multi-center breast ultrasound image segmentation aims to leverage limited labeled data from a single center to enhance model discriminability across unlabeled data from other centers. However, differences in equipment parameters, disease severity, and imaging conditions collectively contribute to significant cross-domain shifts in multi-center data. In a spirit of the golden mean, we argue that constructing an intermediate domain between the source and target domains can effectively improve model generalization. Therefore, we propose a Cross-domain Few-label Generalization (CFG) framework for multi-center breast ultrasound image segmentation. Specifically, we design the Intermediate Domain Generator (IDG) to generate intermediate domain samples that contain features from both the source and target domains bidirectionally, enabling the model to explicitly learn universal semantic representations. Additionally, we apply Swin Masked Autoencoder (MAE) to mask and reconstruct ultrasound images, simulating speckle noise encountered during clinical ultrasound acquisition, thereby increasing the diversity of intermediate domain samples. Furthermore, we integrate the Kolmogorov-Arnold Network (KAN) with UNet to construct KAN-UNet, integrating learnable spline functions directly onto the edges, enabling effective multi-scale perception of breast cancer lesion features. Experimental results show that even with limited labeled data from the source domain (BUSI-WHU), the CFG framework achieves a Kappa value of 77.17%, surpassing ten state-of-the-art methods and outperforming the second-best method by 0.78% across four multi-center ultrasound datasets (BUSI-WHU, BUSI, Dataset-B, and Dataset-C) collected from different medical centers.

Change detection (CD) aims to identify and locate changes in the same observed surface coverage area across bitemporal images. This technique has widespread applications in urban planning, land use, and disaster damage extraction. Deep learning-based CD methods typically use learnable encoders to map bitemporal images to a common domain distribution space, allowing for the discrimination and localization of change and invariant features. However, due to differences in imaging mechanisms, seasons, and shooting angles, a large number of pseudochanges may easily appear, affecting the accurate recognition of the domain distribution space. In addition, binary CD focuses solely on whether scene targets have changed, resulting in change labels that encompass a variety of different objects, thus increasing the significance of intraclass differences. To address the aforementioned issues, we propose a domain distribution reconstruction learning (DDRL) framework for binary CD, which effectively mitigates the problem of pseudochanges by detecting abnormal feature domain distributions. Specifically, DDRL first extracts multiscale features from bitemporal images using a Siamese cross-window self-attention module, achieving feature domain transformation from the original space. Subsequently, it employs a graph attention enhanced (GAE) module to improve the low-level domain distribution, enabling it to focus on change regions. In addition, DDRL utilizes a cross-domain feature contrastive learning (CFCL) module for reconstructive learning of high-level fused features. This process ensures that intraclass features are compact, while interclass features are dispersed within the high-level domain distribution, thereby significantly improving the domain distribution representation to discriminate pseudochanges. Experimental results show that the proposed DDRL performs excellently across multiple public datasets, surpassing mainstream methods and significantly improving CD performance. The source code will be made available athttps://github.com/yzygit1230/DDRL.

Breast cancer is one of the most prevalent diseases for women worldwide. Early and accurate ultrasound image segmentation plays a crucial role in reducing mortality. Although deep learning methods have demonstrated remarkable segmentation potential, they still struggle with challenges in ultrasound images, including blurred boundaries and speckle noise. To generate accurate ultrasound image segmentation, this paper proposes the Edge-Aware Multi-Scale Group-Mix Attention Network (EMGANet), which generates accurate segmentation by integrating deep and edge features. The Multi-Scale Group Mix Attention block effectively aggregates both sparse global and local features, ensuring the extraction of valuable information. The subsequent Edge Feature Enhancement block then focuses on cancer boundaries, enhancing the segmentation accuracy. Therefore, EMGANet effectively tackles unclear boundaries and noise in ultrasound images. We conduct experiments on two public datasets (Dataset-B, BUSI) and one private dataset which contains 927 samples from Renmin Hospital of Wuhan University (BUSI-WHU). EMGANet demonstrates superior segmentation performance, achieving an overall accuracy (OA) of 98.56%, a mean IoU (mIoU) of 90.32%, and an ASSD of 6.1 pixels on the BUSI-WHU dataset. Additionally, EMGANet performs well on two public datasets, with a mIoU of 88.2% and an ASSD of 9.2 pixels on Dataset-B, and a mIoU of 81.37% and an ASSD of 18.27 pixels on the BUSI dataset. EMGANet achieves a state-of-the-art segmentation performance of about 2% in mIoU across three datasets. In summary, the proposed EMGANet significantly improves breast cancer segmentation through Edge-Aware and Group-Mix Attention mechanisms, showing great potential for clinical applications.

Pathology image matching is crucial for assisting pathologists in the comprehensive diagnosis of cancerous areas. However, variations in image rotation and staining caused by inherent slide imaging techniques increase the burden on pathologists, complicating the examination of cancer across different pathology slides. To address this challenge, we introduce multi-scale ring rotation-invariant matching (MRRM), which improves image matching efficiency using ring topology, assisting pathologists in robustly aligning biomarker information across various pathology images. Specifically, by employing multi-scale rings as convolution kernels, we accurately locate keypoints from the differencing of the ring pyramid, which not only enhances the likelihood of successful pathology image matching but also supports our feature descriptor in achieving advantageous performance in rotation-invariance. Experiments show that with manually annotated golden landmarks as the standard in 81 cases, exhibiting significantly superior matching accuracy (130.93 $\,\mu \mathrm{m}$) and a success rate of 93.83% compared to other methods, particularly in cases with rotated pathology images. This meets the routine diagnostic requirements of pathologists for cancer diagnosis.

Significant challenges exist in the registration of multimodal images (MMIs) due to nonlinear radiation differences, variations in lighting, and interference from image noise. These issues often lead to unreliable similarity measurements and low accuracy in point matching during multimodal registration. To address these challenges, this letter introduces a novel MMI registration method based on adjacent self-similarity 3-D convolution (ASTC). The proposed method consists of three main steps: feature point extraction, where key points are uniformly extracted via the block-FAST method; ASTC salient feature construction, where a local adjacent self-similarity (ASS) model is employed to create multidimensional features; and feature structure enhancement, where a 3-D convolution is used for feature enhancement and finishing the process of image feature description. This letter evaluates the ASTC method against six sets of representative MMIs and compares it with six other algorithms. The results demonstrate that: 1) the ASTC algorithm effectively overcomes radiation distortion, intensity differences, and lighting differences in MMIs, leading to improved accuracy in point matching; and 2) the ASTC algorithm achieves higher matching efficiency and reduces time consumption, making it a practical choice for various data types. In summary, the proposed ASTC algorithm offers a robust solution for reliable registration of MMIs, addressing common challenges related to image differences and improving the overall accuracy of the process. The experimental data and code link used in this letter can be found athttps://github.com/yangwill81/ASTC.

Semantic change detection (SCD) has gradually emerged as a prominent research focus in remote sensing image processing due to its critical role in earth observation applications. In view of its powerful semantic-driven feature extraction capability, the Segment Anything Model (SAM) has demonstrated its suitability across various visual scenes. However, it suffers from significant performance degradation when confronted with remote sensing images, especially those containing various ground objects that possess significant inter-class similarity and substantial intra-class variations. To address the above issues, we propose SCD-SAM, aiming to leverage the potent visual recognition capabilities of SAM for enhanced accuracy and robustness in SCD. Specifically, we introduce a contextual semantic change-aware dual encoder that combines MobileSAM and CNN to extract progressive semantic change features in parallel, and inject local features into the MobileSAM encoder through depth feature interaction to compensate for the Transformer’s limitations in perceiving local semantic details. Besides, in order to utilize the strong visual feature extraction capability of MobileSAM in remote sensing images, we propose a semantic adaptor that aggregates semantic-oriented information about changing objects. To better integrate the extracted contextual semantic information, we devise a progressive feature aggregation dual decoder that aggregates binary change features and semantic change features respectively, alleviating the semantic gap across different scales. The quantitative and visual results show that SCD-SAM outperforms the state-of-the-art SCD methods on publicly open SCD datasets (e.g., SECOND-CD and Landsat-CD). The code will be made available at https://github.com/yzygit1230/SCD-SAM.

Matching whole slide histopathology images to provide comprehensive information on homologous tissues is beneficial for cancer diagnosis. However, the challenge arises with the Giga-pixel whole slide images (WSIs) when aiming for high-accuracy matching. Learning-based methods are difficult to generalize well with large-size WSIs, necessitating the integration of traditional matching methods to enhance accuracy as the size increases. In this paper, we propose a multi-size guiding matching method applicable high-accuracy requirements. Specifically, we design learning multiscale texture to train deep descriptors, called TDescNet, that trains 64 × 64 × 256 and 256 × 256 × 128 size convolution layer as C64 and C256 descriptors to overcome staining variation and low visibility challenges. Furthermore, we develop the 3D-ring descriptor using sparse keypoints to support the description of large-size WSIs. Finally, we employ C64, C256, and 3D-ring descriptors to progressively guide refined local matching, utilizing geometric consistency to identify correct matching results. Experiments show that when matching WSIs of size 4096 × 4096 pixels, our average matching error is 123.48 μm and the success rate is 93.02 % in 43 cases. Notably, our method achieves an average improvement of 65.52 μm in matching accuracy compared to recent state-of-the-art methods, with enhancements ranging from 36.27 μm to 131.66 μm. Therefore, we achieve high-fidelity whole-slice image matching, and overcome staining variation and low visibility challenges, enabling assistance in comprehensive cancer diagnosis through matched WSIs.

Recently, convolutional neural networks (CNNs) have achieved impressive progress in multi‐focus image fusion (MFF). However, it always fails to capture sufficient discrimination features due to the local receptive field limitations of the convolutional operator, restricting most current CNN‐based methods’ performance. To address this issue, by leveraging self‐attention (SA) mechanism, the authors propose Siamese SA network (SSAN) for MFF. Specifically, two kinds of SA modules, position SA (PSA) and channel SA (CSA) are utilised to model the long‐range dependencies across focused and defocused regions in the multi‐focus image, alleviating the local receptive field limitations of convolution operators in CNN. To search a better feature representation of the input image for MFF, the captured features obtained by PSA and CSA are further merged through a learnable 1 × 1 convolution operator. The whole pipeline is in a Siamese network fashion to reduce the complexity. After training, the authors SSAN can accomplish well the fusion task with no post‐processing. Experiments demonstrate that their approach outperforms other current state‐of‐the‐art methods, not only in subjective visual perception but also in the quantitative assessment.

We study the problem of multi-focus image fusion, where the key challenge is detecting the focused regions accurately among multiple partially focused source images. Inspired by the conditional generative adversarial network (cGAN) to image-to-image task, we propose a novel FuseGAN to fulfill the images-to-image for multi-focus image fusion. To satisfy the requirement of dual input-to-one output, the encoder of the generator in FuseGAN is designed as a Siamese network. The least square GAN objective is employed to enhance the training stability of FuseGAN, resulting in an accurate confidence map for focus region detection. Also, we exploit the convolutional conditional random fields technique on the confidence map to reach a refined final decision map for better focus region detection. Moreover, due to the lack of a large-scale standard dataset, we synthesize a large enough multi-focus image dataset based on a public natural image dataset PASCAL VOC 2012, where we utilize a normalized disk point spread function to simulate the defocus and separate the background and foreground in the synthesis for each image. We conduct extensive experiments on two public datasets to verify the effectiveness of the proposed method. Results demonstrate that the proposed method presents accurate decision maps for focus regions in multi-focus images, such that the fused images are superior to 11 recent state-of-the-art algorithms, not only in visual perception, but also in quantitative analysis in terms of five metrics.