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.

Gas detection is critically important in both industrial production and environmental monitoring. Due to the absorption characteristics of gases in the infrared wavelength domain, thermal infrared multispectral video imaging provides a convenient data sensing method for rapid, large-scale gas detection. In the process of gas detection, the shape of the detected gas leakage region is often distorted by the irregular motion patterns of gases. Although some studies have considered temporal motion information, these spatial-temporal feature extractors were designed originally for regular and salient objects such as vehicles and pedestrians, but not for gas. Moreover, the problem of weak gas signal and lack of a large well-labeled dataset also hinders the development of gas detection with deep learning. Regarding the above issues, a novel gas detection network, Computational Fluid Dynamics neural Network (CFDNet), was proposed for infrared multispectral gas detection. Firstly, to better fit the motion patterns of gas and keep the shape of the detected leakage region, a spatial-temporal fluid motion feature extractor, Computational Fluid Dynamics (CFD) Basic Block, was proposed. CFD Basic Block diffuses and displaces high-dimensional features through a convolution based on the Navier-Stokes equations in computational fluid dynamics. Secondly, to enhance the signal of gaseous targets, a Local Entropy and combination Difference (LED) data feature enhancement module was designed based on the instrument characteristics and local entropy information. Finally, a simulated dataset and a transfer learning framework were built to train a deep learning gas detection model with great generalizability. Experiments show that the proposed method, CFDNet, achieves a better performance than existing approaches. On the real-world dataset used for testing, it reaches an IoU of 50.128%, a Kappa of 59.902%, and an F1 Score of 44.885%. CFDNet demonstrates an excellent performance on keeping the shape of the detected gas leakage region under irregular motion patterns, especially for gas plumes with small area-ratio and low signal-noise-ratio.

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.

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.

The microscopic urine sediment examination is a crucial part in the evaluation of renal and urinary tract diseases. Recently, there are emerging CNNs-based detectors to detect the urine sediment particles in an end-to-end manner. However, it is not very compatible to transfer CNNs-based detector directly from natural images application to microscopic images, especially in which small objects are in majority. This paper proposes a bidirectional context propagation network called BCPNet for urine sediment particle detection. In BCPNet, spatial details encoded by shallow convolutional layers are propagated upward to improve the localisation ability of deep features. On the contrary, high semantic information encoded by deep convolutional layers is propagated downward to enhance the distinctiveness of shallow features. With the refinement by convolutional block attention modules, the enriched features are more powerful to both localisation and classification. Experimental results on urine sediment particle dataset USE demonstrate effectiveness of the proposed BCPNet.